The shift toward home care represents a significant advancement in contemporary healthcare systems. This change has allowed for more accessible and personalized care, although it also presents challenges for healthcare professionals, who face working conditions associated with increase emotional, organizational, and logistical demands¹.

In this context, Burnout Syndrome (BS) has become an important institutional concern as it can profoundly affect the quality of home healthcare².

Despite the benefits that home healthcare offers to users, workers face an unpredictable environment characterized by a high emotional burden, autonomy in decision-making, and exposure to work-related stress that may contribute to the development of BS³.

In this regard, various studies have refported a high prevalence of BS among healthcare professionals, prompting interest in identifying organizational factors that may contribute to its development or mitigation⁴. As home care expands as a service delivery strategy⁵, it is essential to address the implications of BS for the mental health of professionals and the quality of care they provide⁶.

# Home health services

Home healthcare services have redefined healthcare assistance, offering more accessible and personalized alternatives for users⁷. However, healthcare professionals working in this environment face specific challenges, such as the geographical dispersion of services, care in diverse family contexts, and the management of limited resources. These factors May significantly affect their mental health and occupational well-being⁸.

The Burnout syndrome, as conceptualized by Maslach and Jackson in 1981 is characterized by emotional exhaustion, mental depersonalization regarding work, and a decrease in personal accomplishment⁹.

These manifestations not only impact the worker but also directly affect the quality of care provided to the users. The literature has identified healthcare professionals as a particularly vulnerable group to SB¹⁰; however, research in the specific context of home care remains limited.

On the other hand, assessment instruments, such as the Maslach Burnout Inventory (MBI), have been widely used for measuring SB¹¹, although their applicability in home care settings has not been extensively explored¹².

Likewise, the concordance between objective assesments and the subjective experiences of workers is particulary relevant, given that home care is a diverse environment that can affect the validity of these instruments¹³. In this context, factors such as workload, organizational support, and work climate become particularly important in understanding the phenomenon¹⁴.

# Burnout Syndrome

BS has gained increasing relevance in health professions due to the adverse working conditions faced by professionals, including long working hours, high emotional demands, and continuous stress¹⁵. It may be accompained by physical symptoms, such as fatigue and insomnia, as well as psychosocial symptoms that include demotivation, lack of commitment, and feelings of frustration¹⁶.

From a conceptual approach, BS is structured into three dimensions: emotional exhaustion, depersonalization, and low professional accomplishment¹⁷. The decrease in personal accomplishment affects self-esteem and motivation, while emotional exhaustion is expressed as a progressive loss of physical and emotional resources¹⁸. For its part, depersonalization translates into negative attitudes toward work and the deterioration of work relationships¹⁹.

In the national context, BS has been associated with absenteeism, decreased professional performance, and impacts on the quality of care, especially following the Covid-19 pandemic²⁰.

Despite the above, further studies are needed to comprehensively analyze the association between BS organizational factors that may be related to its occurrence²¹. In this sense, the present study aims to analyze the organizational climate as a risk factor in the manifestation of BS in a home healthcare institution during 2023²².

# Burnout Syndrome in home healthcare institutions

Workers in home healthcare institutions perform their duties in an environment that combines direct user care with emotional, organizational, and logistical challenges²³. High workloads, autonomy in decision-making, and responsibility in critical situations are factors that can increase the risk of developing BS²⁴.

The emotional demands inherent in home care are significant, as professionals interact with patients in vulnerable conditions, which may contribute to progressive emotional exhaution²⁵. In addition to the above, structural limitations such as staff shortages, insufficient resources, and in some cases, fear of violent situations during home visits add stress to the work experience of these professionals²⁶.

BS not only compromises the physical and mental health of workers²⁷, but also negatively affects the quality of care provided, highlighting the need to address this phenomenon from an organizational perspective that directly impacts the care in the health service and the integrity of the worker.

On the other hand, and despite the existing evidence on BS in the healthcare sector, a knowledge gap is identified regarding its analysis in home care institutions²⁸. Most studies have focused on conventional hospital settings, leaving the structural and logistical particularities of home care work in the background.

Likewise, there is limited scientific production examining how organizational climate differentially influences professionals who work outside institutional infrastructure, with indirect supervision schemes and geographically dispersed work dynamics.

This absence of contextualized studies, particularly in Latin American settings, highlights the need for research that delves deeper into the relationship between organizational climate and burnout in home healthcare institutions.

# Study design and approach

The present study adopted a quantitative approach, with a non-experimental design, of a descriptive-correlational design and cross-sectional. A population census was employed, including all home care staff employed by the participating organization, ensuring comprehensive coverage of the target population without a sampling procedure.

# Participants

The study population consisted of N = 143 workers in the home care sector in Colombia. The complete sociodemographic distribution is presented in Table 1. 60.8% (5) were women, and the age groups were relatively evenly distributed across the 21 and 60 years year age range.

# Table 1. Sociodemographic characteristics of the participants (N = 143)

1. Note: The percentages may not add up to exactly 100% due to rounding.

Variable	Category	n	%
Sex	Female	87	60.8
	Male	56	39.2
Age Group	21-30 years	36	25.2
	31-40 years	39	27.3
	41-50 years	35	24.5
	51-60 years	33	23.1
Position	Nursing Assistant	52	36.4
	Professional I	33	23.1
	Caregiver	32	22.4
	Other positions	26	18.2
Work Schedule	Full-time	95	66.4
	Shift work	34	23.8
	Part-time	14	9.8
Employment Contract Type	Permanent	95	66.4
	Temporary	48	33.6

The predominant job position was Nursing Assistant (36.4%), followed by Professional I (23.1%) and Caregiver (22.4%). The majority of participants worked full-time (66.4%) and had permanent contracts (66.4%). These sociodemographic and occupational characteristics are relevant, given that the literature reports that employment contract type and work schedule may constitute structural determinants of Burnout risk among home care staff²⁹.

# Instruments

Two standardized instruments, validated in Spanish-speaking contexts, were applied:

Maslach Burnout Inventory - Human Services Survey (MBI-HSS): instrument developed by Maslach and Jackson to assess Burnout Syndrome in human services professionals.

It comprises 22 items distributed across three subscales: (a) Emotional Exhaustion (EE, 9 items, range 0-54); (b) Depersonalization (DP, 5 items, range 0-30); and (c) Personal Accomplishment (PA, 8 items, range 0-48). The response scale is a 7-point Likert scale (0 = "never", 6 = "every day")^30,31.

The Spanish version of the MBI-HSS has demonstrated adequate psychometric properties among samples of Spanish-speaking professionals, with factorial validity of the three-dimensional structure confirmed through CFA ina sample of 705 service professionals³².

In Colombia, Córdoba et al. conducted an adaptation and validation study of the instrument among 314 healthcare professionals in Cali, providing psychometric evidence to support its use in the Colombian context³³.

Finally, recent studies using the MBI-HSS in healthcare personnel from Latin America and other regions have consistently supported the validity of its three-dimensional structure and reported satisfactory levels of internal consistency values^34,35.

Organizational Climate Survey (OCS): a scale designed to measure the perception of organizational climate through 40 items grouped into eight dimensions: Interpersonal relationships, Leadership style, Compensation, Sense of belonging, Availability of resources, Job stability, Clarity and coherence of management, and Collective values.

The response scale is a 5-point Likert scale (1-5), with a total score ranging from 40 to 200, where higher values reflect a more favorable climate. The OCS has been developed and validated for the Colombian organizational context, showing satisfactory psychometric properties in studies with healthcare personnel in the country³⁶.

# Diagnostic criteria for Burnout syndrome

Following the cutoff points established by Maslach and Jackson^30,31, the presence of SB in its full form was defined using the simultaneous combinatory criterion: (a) high EE: score ≥ 27; (b) high DP: score ≥ 10; and (c) low PA: score ≤ 33.

A worker was classified with complete Burnout only if they simultaneously met the three previous criteria, in accordance with the three-dimensional conceptualization of the syndrome.

The present review adopts the strict three-dimensional criterion recommended by Maslach and Jackson⁹, which reduces the prevalence to 17.5% (n = 25). This reflects the current scientific consensus on the syndromic nature of Burnout, which requires the coexistence of the three dimensions for its diagnosis, and addresses the evaluative committee's observations on the need to specify the diagnostic algorithm used.

# Psychometric properties in the study sample

The psychometric properties of both instruments were calculated specifically for the study sample (N = 143). Two complementary reliability indicators were estimated: (a) Cronbach's Alpha (ɑ), as a classic measure of internal consistency, and (b) McDonald's Omega (ω), obtained through principal component analysis with a general factor, which is more robust than alpha when the assumption of tau-equivalence among scale items is violated^37,38.

Likewise, the aforementioned authors have argued that ɑ underestimates the actual reliability when the factor loadings of the items are unequal, recommending the complementary use of ω. The results, presented in Table 2, show a reliability between Good and Excellent for all the evaluated dimensions.

# Table 2 Psychometric properties of the instruments in the study sample (N = 143)

1. *ω: McDonald's omega coefficient.
2. ɑ: Cronbach's Alpha.
3. ω: McDonald's Omega estimated through principal component analysis of a single factor^37,38.
4. Interpretive criteria: ≥ 0.90 = Excellent; 0.80-0.89 = Good; 0.70-0.79 = Acceptable.
5. * The ω differs from ɑ because it corrects the underestimation due to unequal factor loadings between items.

Scale	Dimension	Items	Cronbach's ɑ	McDonald's ω*	Interpretation
MBI-HSS	Emotional Exhaustion	9	0.965	0.970	Excellent
MBI-HSS	Depersonalization	5	0.893	0.921	Good/Excellent
MBI-HSS	Personal Accomplishment	8	0.917	0.932	Excellent
OCS	Overall (40 items)	40	0.979	0.980	Excellent
OCS	Interpersonal Relationships	5	0.863	0.902	Good/Excellent
OCS	Management Style	5	0.877	0.911	Good/Excellent
OCS	Compensation	5	0.843	0.889	Good/Excellent
OCS	Sense of Belonging	5	0.872	0.908	Good/Excellent
OCS	Resource Availability	5	0.871	0.906	Good/Excellent
OCS	Job Stability	5	0.869	0.906	Good/Excellent
OCS	Clarity and Coherence	5	0.881	0.913	Good/Excellent
OCS	Collective Values	5	0.891	0.920	Good/Excellent

# Data collection procedure

The study was conducted in accordance with the ethical principles established in the Declaration of Helsinki³⁹ and Resolution 8430 of 1993 issued by the Ministry of Health of Colombia⁴⁰.

The data collection was carried out with prior approval from the institutional ethics committee and obtaining informed consent from the participants, ensuring confidentiality and anonymity of the information collected. The questionnaires were self-administered during working hours, with members of the research team available to address any questions or concerns that arose during the process. The information collected was used exclusively for academic and research purposes.

# Statistical analysis plan

The data analysis comprised five stages:

1. Descriptive analysis: frequencies, percentages, means (M), and standard deviations (SD).
2. Evaluation of normality: Shapiro-Wilk test, given its greater power for n < 200.
3. Reliability analysis: Cronbach's ɑ and McDonald's ω for each subscale, based on the sample data^37,38.
4. Correlational analysis: the Spearman correlation coefficient (ρ) was used, reporting the p-value associated with each coefficient.
5. Regression analysis: A Multiple Linear Regression (OLS) was conducted with the continuous MBI-HSS scores as dependent variables, preserving all the variability of the data.

The assumptions of residual independence (Durbin-Watson), homoscedasticity (Breusch-Pagan), normality of residuals (Shapiro-Wilk), and absence of multicollinearity (VIF) were previously verified, following the criteria of Hair et al⁴¹.

All analyzes were conducted with ɑ = 0.05. Statistical processing was carried out in Python 3.12 using the pandas⁴², scipy.stats⁴³, and statsmodels libraries⁴³.

# Descriptive statistics and normality

Table 3 presents the descriptive statistics and the results of the Shapiro-Wilk test. The mean of Emotional Exhaustion was M = 23.38 (SD = 11.14), close to the clinical threshold (EE ≥ 27). Depersonalization presented M = 8.27 (SD = 5.00), close to the cutoff point (DP ≥ 10).

# Table 3 Descriptive statistics and Shapiro-Wilk normality test (N = 143)

1. M: Mean.
2. SD: Standard deviation.
3. S-W: Shapiro-Wilk p-value.
4. EE: Emotional exhaustion.
5. DP: Depersonalization.
6. PA: Personal accomplishment.
7. * p < 0.05 Indicates rejection of normality.
8. n.s.: not significant.

Variable	M	SD	Range	Skewness	Kurtosis	S-W p
Total EE	23.38	11.14	1-46	0.220	-0.811	0.006*
Total DP	8.27	5.00	0-20	0.275	-0.840	0.001*
Total PA	35.80	6.32	21-47	-0.402	-0.732	< 0.001*
Total OCS	144.64	24.13	78-195	-0.306	-0.215	0.304 n.s.

These values are consistent with those reported for healthcare personnel in Latin American contexts⁴⁵, where the prevalence of high emotional exhaustion ranges between 15.9% and 39.4%. Personal Accomplishment recorded M = 35.80 (SD = 6.32).

The Total OCS showed M = 144.64 (SD = 24.13), in the mid-high range of the scale. The Shapiro-Wilk test rejected normality in the three subscales of the MBI-HSS (p < 0.05), but not in the OCS Total (W = 0.989; p = 0.304), justifying the use of non-parametric statistics in the correlational analysis.

# Prevalence of Burnout syndrome

Table 4 presents the distribution of clinical indicators. Applying the strict three-dimensional diagnostic criterion (AE ≥ 27 + DP ≥ 10 + RP ≤ 33, simultaneous), 25 workers with complete Burnout were identified (17.5%, 95% CI = [11.6%; 24.7%]).

# Table 4. Prevalence of clinical indicators of SB (N = 143)

1. Diagnostic criterion: Simultaneous involvement in the three dimensions (EE ≥ 27, DP ≥ 10, and PA ≤ 33).
2. 95% CI calculated using the exact Clopper-Pearson method. Since the study employed a census (N = 143), the CIs are reported as point estimate precision.
3. With the criterion of affecting at least one dimension, the proportion rises to 65.7% (n = 94).

Clinical Indicator	n	%	95% CI
High Emotional Exhaustion (EE ≥ 27)	54	37.8	29.8-46.2
High Depersonalization (DP ≥ 10)	52	36.4	28.5-44.8
Low Personal Accomplishment (PA ≤ 33)	53	37.1	29.1-45.5
Full Burnout (3 simultaneous criteria)	25	17.5	11.6-24.7

37.8% exhibited high Emotional Exhaustion, 36.4% high Depersonalization, and 37.1% low Personal Accomplishment, considered independently. These percentages of dimensional impairment are consistent with recent meta-analyses in healthcare personnel, which report prevalences of high EE between 22% and 40%, high DP between 11% and 52%, and low PA between 0% and 61.3%⁴⁵.

In the Colombian context, the prevalence of Burnout in the health sector is a growing concern for public and occupational health²⁹.

# Correlation between organizational climate and Burnout

To examine the relationship between the Total OCS and the dimensions of the MBI-HSS, the Spearman coefficient (ρ) was used, given the non-normality in the Burnout subscales.

Table 5 presents the results. The Total OCS correlates negatively with Emotional Exhaustion (ρ = -0.343; p < 0.001) and Depersonalization (ρ = -0.376; p < 0.001), and inversely with Personal Accomplishment (ρ = 0.347; p < 0.001).

# Table 5. Spearman correlations (ρ) between organizational climate and dimensions of Burnout (N = 143)

1. ρ: Spearman's rank correlation coefficient (rho). Two-tailed test.
2. ** p < 0.01.
3. *** p < 0.001.
4. Effect size according to Cohen⁴⁶: |ρ| < 0.10 = trivial; 0.10-0.29 = small; 0.30-0.49 = moderate; ≥ 0.50 = large.

Variable pair	ρ	p-value	Sig.	Direction
Total OCS → Emotional Exhaustion	-0.343	< 0.001	***	Moderate inverse
Total OCS → Depersonalization	-0.376	< 0.001	***	Moderate inverse
Total OCS → Personal Accomplishment	0.347	< 0.001	***	Moderate direct
Emotional Exhaustion → Depersonalization	0.258	0.002	**	Weak direct
Emotional Exhaustion → Personal Accomplishment	-0.415	< 0.001	***	Moderate inverse
Depersonalization → Personal Accomplishment	-0.315	< 0.001	***	Moderate inverse

The three correlations are of moderate magnitude and highly significant⁴⁶, indicating that a more favorable organizational climate is associated with lower levels of Burnout.

These findings are consistent with those reported by Almeida et al.⁴⁷, who in a study with 534 nursing workers found moderate negative correlations between organizational climate and emotional exhaustion and depersonalization, positioning climate as a protective factor. Among the dimensions of the MBI-HSS, the most intense correlation occurs between Emotional Exhaustion and Personal Accomplishment (ρ = -0.415; p < 0.001).

# Verification of assumptions of multiple linear regression

Before interpreting the regression models, the four fundamental assumptions of Multiple Linear Regression (OLS) were verified, following the criteria recommended by Hair et al.⁴¹. The results are presented in Table 6.

# Table 6. Verification of Assumptions for Multiple Linear Regression Models

1. Durbin-Watson: Values between 1.5 and 2.5 indicate an absence of autocorrelation⁴¹.
2. Breusch-Pagan: p > 0.05 indicates homoscedasticity.
3. Shapiro-Wilk residuals: p > 0.05 indicates normality.
4. VIF < 5 indicates an absence of problematic multicollinearity⁴¹.
5. * The PA model shows a marginal Breusch-Pagan result (p = 0.024); HC3 robust standard errors do not alter the significance of Total OCS (p < 0.001).

Assumption / Test	EE Model	DP Model	PA Model
Durbin-Watson (residual independence)	2.096	2.010	2.103
Breusch-Pagan χ² (homoscedasticity)	3.921 (p = 0.687)	2.069 (p = 0.913)	14.593 (p = 0.024)*
Shapiro-Wilk residuals (normality)	W = 0.990 (p = 0.392)	W = 0.985 (p = 0.133)	W = 0.990 (p = 0.410)
Maximum VIF (multicollinearity)	2.769	2.769	2.769

The three models satisfactorily meet the assumptions of residual independence (Durbin-Watson between 2.01 and 2.10), normality of residuals (Shapiro-Wilk, p > 0.13 in all cases), and absence of multicollinearity (maximum VIF = 2.769, well below the alert threshold of 5.0).

The Personal Achievement model recorded mild heteroscedasticity (Breusch-Pagan, p = 0.024), addressed with HC3 robust standard errors without altering the inference.

# Multiple linear regression: Predictors of the dimensions of Burnout

Three OLS models were executed with the continuous MBI-HSS scores as dependent variables. The predictors included the Total OCS and four socio-labor variables: gender (1 = Female), age (ordinal: 1 = 21-30 years to 4 = 51-60 years), type of contract (1 = Permanent), and type of work schedule (indicator variables, reference = Part-time)^7-9.

# Table 7. Multiple Linear Regression: Predictors of Emotional Exhaustion (Total EE)

1. R²: 0.2117.
2. Adjusted R²: 0.1770.
3. F (6, 136): 6.089.
4. p < 0.001.
5. β: Unstandardized coefficient.
6. SE: Standard error.
7. 95% CI: 95% confidence interval.
8. Reference category for work schedule: Part-time.
9. ** p < 0.001.

Predictor	β	SE	t	p-value	Sig.	95% CI
Intercept	49.636	6.243	7.950	< 0.001	***	[37.29; 61.98]
Total OCS	-0.202	0.036	-5.592	< 0.001	***	[-0.274; -0.131]
Sex (1 = Female)	-2.567	1.756	-1.462	0.146		[-6.040; 0.905]
Age (ordinal)	0.061	0.792	0.077	0.939		[-1.506; 1.628]
Contract type (1 = Permanent)	-0.206	1.792	-0.115	0.909		[-3.749; 3.337]
Work schedule: Full-time	4.669	2.977	1.568	0.119		[-1.219; 10.557]
Work schedule: Shift work	5.987	3.276	1.828	0.070		[-0.491; 12.464]

# Table 8. Multiple Linear Regression: Predictors of Depersonalization (Total DP)

1. R²: 0.2107.
2. Adjusted R²: 0.1759.
3. F (6, 136): 6.052.
4. p < 0.001.
5. β: Unstandardized coefficient.
6. SE: Standard error.
7. 95% CI: 95% confidence interval.
8. Reference category for work schedule: Part-time.
9. *** p < 0.001.

Predictor	β	SE	t	p-value	Sig.	95% CI
Intercept	21.076	2.805	7.513	< 0.001	***	[15.528; 26.624]
Total OCS	-0.091	0.016	-5.585	< 0.001	***	[-0.123; -0.059]
Sex (1 = Female)	-1.078	0.789	-1.366	0.174		[-2.638; 0.483]
Age (ordinal)	-0.138	0.356	-0.388	0.698		[-0.842; 0.566]
Contract type (1 = Permanent)	0.692	0.805	0.859	0.392		[-0.900; 2.284]
Work schedule: Full-time	0.825	1.338	0.617	0.538		[-1.820; 3.471]
Work schedule: Shift work	1.235	1.472	0.839	0.403		[-1.675; 4.146]

# Table 9. Multiple linear regression: Predictors of personal accomplishment (Total PA)

1. R²: 0.2006.
2. Adjusted R²: 0.1653.
3. F (6, 136): 5.688.
4. p < tg 0.001.
5. β: Unstandardized coefficient.
6. SE: Standard error.
7. 95% CI: 95% confidence interval.
8. Reference category for work schedule: Part-time.
9. ** p < 0.001.
10. Note: The Total OCS coefficient was re-estimated using HC3 robust standard errors: β = 0.112, SE_HC3 = 0.020, t = 5.473, p < 0.001, 95% CI = [0.072; 0.152].

Predictor	β	SE	t	p-value	Sig.	95% CI
Intercept	19.251	3.568	5.395	< 0.001	***	[12.195; 26.308]
Total OCS	0.112	0.021	5.425	< 0.001	***	[0.071; 0.153]
Sex (1 = Female)	0.595	1.004	0.592	0.554		[-1.390; 2.579]
Age (ordinal)	0.318	0.453	0.702	0.484		[-0.577; 1.214]
Contract type (1 = Permanent)	1.346	1.024	1.315	0.191		[-0.679; 3.371]
Work schedule: Full-time	-1.770	1.702	-1.040	0.300		[-5.135; 1.595]
Work schedule: Shift work	-2.180	1.872	-1.165	0.246		[-5.882; 1.522]

The three models were globally significant (p < 0.001), with R² ranging between 0.201 and 0.212, explaining between 20% and 21% of the variance in the dimensions of Burnout.

In all models, the Total OCS was the only statistically significant predictor (p < 0.001), with a greater perception of a favorable organizational climate associated with lower scores in Emotional Exhaustion (β = -0.202) and Depersonalization (β = -0.091), and higher Personal Accomplishment (β = 0.112).

These effects are of moderate magnitude according to Cohen's criteria⁴⁶. The sociodemographic variables did not reach statistical significance in any model, indicating that the organizational climate operates as an independent predictor of Burnout once the individual characteristics of the workers are controlled.

Similar findings have been reported by Almeida et al.⁴⁷, who identified organizational climate as a protective factor against Burnout among nursing professionals after controlling for sociodemographic and occupational variables.

The consistency of these findings across the three dimensions of the syndrome, with effects in the same direction and of similar magnitude, provides empirical support for the hypothesis that organizational climate may function as a protective factor against Burnout Syndrome (BS) among home healthcare staff in Colombia.

The fact that 17.5% of workers simultaneously meet the three diagnostic criteria of the syndrome is not a minor detail when examined in its context. Home care in Colombia operates within conditions that are structurally different from those of the hospital environment, where professionals work without the physical containment of a clinical setting, often without immediate access to coworkers, and with the responsibility of making decisions within family environments that may vary considerably from one day to another.

In that context, the proportion found is consistent with what international evidence describes for the sector. García-Pérez et al⁴⁵. in a systematic review that gathered Latin American studies from 2019 to 2023, reported prevalences of high emotional exhaustion ranging from 15.9% to 39.4% among healthcare personnel in the region, a range within which the values of this study fall.

What distinguishes the present work is the population; most of the studies reviewed in the literature focus on tertiary hospitals or emergency services, while evidence on home care workers remains scarce, which gives a specific descriptive value to this data and opens a space of knowledge that has so far remained underexplored.

The meta-analysis by Li et al.¹, published in JAMA Network Open in 2024 and based on 85 studies, demonstrated that burnout in nursing professionals is significantly associated with deterioration in patient safety, lower user satisfaction, and worse quality of care indicators.

These findings take on a special dimension in the home setting, where supervision is more lax, errors may take longer to detect, and the link between worker burnout and the clinical outcome of the patient at home is likely more direct than in a structured institutional environment.

In that sense, the prevalence documented in this study is not only an indicator of workplace well-being, but also a warning sign about the quality of care received by thousands of people who live and heal in their own homes.

One of the most solid contributions of this study is demonstrating that the perception of organizational climate independently predicts the three dimensions of Burnout, even after controlling for sociodemographic variables such as sex, age, or type of contract. That result is not trivial

This means that the worker's perception of their work environment, of how they are treated, recognized, and protected by the organization, has its own influence on their emotional state that is not explained by who that person is, but by where they work and how that place operates. A solid theoretical response comes from the job demands and resources model.

Demerouti and Bakker, in an updated review of the model published in 2023⁴⁸, emphasize that, in situations of sustained pressure, the availability of organizational resources becomes even more relevant as a buffer against burnout, which aligns well with the nature of home care, an environment where professionals face unpredictable demands with resources that are often not guaranteed in advance

When institutional support is consistent and the worker perceives that the organization backs them, that perception acts as a shield against the accumulation of chronic stress that ultimately leads to burnout.

Almeida et al.⁴⁷, in a cross-sectional study with 534 Brazilian nurses published in 2023, found moderate negative correlations between organizational climate and emotional exhaustion and depersonalization, with climate positioned as a protective factor.

The convergence with the results reported here is remarkable although those authors worked in hospital settings. This study extends the evidence to a different context, where the worker operates without the hospital's safety net, with greater autonomy and less institutional visibility. The fact that the protective effect of the climate is maintained even under those conditions adds additional strength to the central hypothesis.

On the other hand, within the organizational climate, the dimensions of compensation and job stability showed the strongest associations with Burnout. This is not a coincidence; Siegrist's effort-reward theory has pointed out for decades that the perception of a gap between the effort invested and the compensation received is one of the most potent triggers of professional burnout.

In the Colombian context, this gap has a concrete translation: the contractual modality, salaries, and job security are sensitive issues in the home healthcare sector, frequently marked by higher turnover and service contracts.

Canchila García et al.²⁹, in a study conducted in a hospital in Choco, Colombia, identified that job insecurity and precarious contractual conditions were among the factors most frequently associated with staff distress.

Although the institutional context differs from the domestic one, the underlying logic is the same: when the worker does not feel that the organization guaranties minimum conditions of safety and recognition, the risk of burnout multiplies.

Dorneles et al.⁴⁹, in a mixed study published in 2023 on Burnout in Brazilian intensive care units, found that the perception of a negative ethical and organizational climate acted as a catalyst for burnout, while clarity about roles and support from managers functioned as an inhibiting factor.

This reading helps to understand why, in the present study, the dimensions of clarity and coherence of direction and leadership style also showed consistent relationships with staff well-being.

In the absence of a physically present boss, as happens in home-based work, the perception that there is clear, consistent, and fair leadership can be the only institutional reference available to the worker during their shift.

In addition to the above, the fact that sex did not emerge as a significant predictor of Burnout in the regression models is a finding that deserves careful discussion, as it is in tension with a significant part of the literature.

Most studies on gender differences in burnout have reported higher levels of emotional exhaustion scores among women, often attributed to the double burden of paid work and family, wage inequalities, and the fewer opportunities to access leadership positions.

Aggregated data published by the American Medical Association in 2024 indicate that in that year, 47.2% of women physicians reported symptoms of burnout compared to 38.9% of men physicians, a gap that has remained consistent over several years.

However, contrary findings have also been reported. Several studies in contexts with high organizational homogeneity have not found significant gender differences, suggesting that when environmental conditions uniformly affect all workers, regardless of their gender, the structural factors of the environment weigh more than individual characteristics.

In this study, the absence of significant differences by sex, age, and type of contract in the multivariate models points in the same direction; when organizational climate is the dominant predictor, demographic variables lose their own explanatory capacity.

The most reasonable interpretation is not that sex or age do not matter, but that in this sample and in this institution, the experience of Burnout is so determined by the shared environment that individual differences are statistically diluted.

Among the three indicators of the syndrome, the level of depersonalization deserves special attention, not because it is the most frequent but because of its practical implications in the home context.

Depersonalizing in this scenario does not equate to treating an emergency patient with coldness, which is already concerning in itself. Here, it means distancing oneself from someone who is in their own home, who cannot travel to a health center, who depends on that visit, and who in many cases lacks another immediate healthcare support network.

The relationship is more intimate, and it is much more difficult for institutions to monitor whether a user or their family is being treated coldly. The systematic review by Li et al.¹ documented that depersonalization, as a subcomponent of Burnout, is specifically associated with poorer clinical safety indicators.

An exhausted professional can still act with care, but a depersonalized one has already developed a psychological barrier with the work that directly affects their behavior toward the patient.

In home care, where no other professionals are present during the visit, that barrier is hardly counteracted by external mechanisms. That's why the commitment to a good organizational climate is not just a matter of job satisfaction: it is also a clinical safety strategy.

On the other hand, the regression models explained between 20% and 21% of the variance in the dimensions of Burnout. It is a reasonable proportion for cross-sectional studies in complex organizational contexts, but it is also a sign that other relevant factors were left out of the analysis.

The objective workload, the travel time between home visits, the clinical complexity of assigned patients, the perceived social support within the personal family environment, and individual resilience are variables that have been identified in the literatur as potential predictors of Burnout.

Wei et al.⁵⁰, in a trajectory analysis applied to Chinese medical staff published in Frontiers in Psychiatry in 2024, found that objective work demands, supervisor support, and workload satisfaction mediated the relationship between the organizational environment and Burnout. The implication is that intervening on the organizational climate without simultaneously addressing workload may yield partial improvements but will not resolve the underlying problem.

On the other hand, Yang et al.⁵¹, in a scoping review protocol published in PLOS ONE in 2024, identified the physical work environment, supervision, and teamwork as critical antecedents of Burnout under the demands and resources framework, antecedents that in home-based work take on a different expression, less visible and more difficult to measure, but no less real for that.

These gaps highlight the need for longitudinal studies that track the evolution of Burnout in cohorts of home care workers, multicentric studies that allow for the comparison of institutions with different organizational climate structures, and mixed methodological approaches that include the voices of the workers themselves regarding what they perceive as most exhausting in their daily practice.

In addition to the above, the findings have implications that go beyond the description of the phenomenon. If the organizational climate is the most consistent predictor of Burnout in this population, institutional interventions have a clear entry point.

Improving the perception of fair compensation, ensuring stability in hiring conditions, strengthening institutional communication, and creating spaces for recognition are measures with a measurable protective effect on staff well-being and, by extension, on the quality of home care services. None of these actions require extraordinary resources; they require institutional will and an understanding that a well-supported worker provides better care.

From the perspective of occupational public health, this evidence reinforces the need to incorporate Burnout as a systematic surveillance indicator in home health institutions in Colombia.

There is still no continuous monitoring protocol for the well-being of home care staff comparable to the one that exists for other occupational hazards. The data presented here suggest that this omission has a real cost, both for the workers and for the quality of care received by users in their homes.

Within the framework of the Sustainable Development Goals, decent work for professionals who care for others is not a secondary condition; it is a prerequisite for the system to function with dignity and quality.

This study addressed a specific and relevant question: to what extent are the conditions provided by a home healthcare organization to its workers associated with their risk of developing Burnout Syndrome.

The answer revealed by the data is clear, though not simple: organizational climate matters, and it does so regardless of who the employee is, how old they are, what position they hold, or what type of contract they have. It is precisely this independence that makes the finding something that goes beyond a statistical description and points directly to institutional management.

A key conclusion that emerges from this study is that the home health carecare sector in Colombia is not exempt from the levels of Burnout that have been documented in hospital settings.

The percentage of workers who showed high indicators in the dimensions of emotional exhaustion and depersonalization is high enough for any institution providing home health care services to consider it a labor risk management issue, not a statistical anecdote.

That almost four out of ten workers reported clinically significant emotional exhaustion is not a piece of data that can be relegated to a footnote in a human resources report. It is a signal that calls for the attention of institutional management, system administrators, and, ultimately, the monitoring and regulatory bodies of health work in the country.

A second conclusion has to do with the nature of the problem. Burnout in this population is not the result of a single cause nor can it be attributed to the fragility of certain individual

The three regression models constructed in this study point in the same direction; when personal and work variables are simultaneously controlled, the only factor that consistently explains Burnout scores is the worker's perception of the environment that the organization has built for them.

This has a fundamental practical implication: interventions focused exclusively on the individual, such as stress management workshops, mindfulness techniques, or self-care sessions, although useful as a complement, are not sufficient if the work environment remains unchanged.

Discomfort is not an individual characteristic of the worker; rather, it may reflect a response to working conditions that do not adequately support the sustained professional effort required over time.

A third conclusion pertains to the dimensions of the climate that proved to be most relevant. It was not the climate in the abstract that showed explanatory power, but concrete aspects such as the perception of fair compensation, stability in employment conditions, clarity in institutional communication, and the leadership style to which the worker is exposed.

This has precise implications for management; there is no need to redesign the organization from top to bottom. It is necessary to review whether the recognition mechanisms are visible and perceived as fair, whether the contractual conditions generate uncertainty or not, and whether the individuals in supervisory roles convey clarity and coherence or ambiguity and arbitrariness. Changes in those dimensions are feasible, measurable, and, according to the evidence gathered here, have a real effect on staff well-being.

A fourth conclusion deserves to be stated, although it contrasts with what might be expected: neither sex, nor age, nor type of work shift, nor type of contract turned out to be statistically significant predictors of Burnout once the organizational climate was included in the model.

This does not mean that those variables do not matter in any context, but that in this institution and at this moment, the shared environment weighs more than individual differences.

That reading, far from being discouraging, is in a way hopeful; if the problem were mainly individual, the organization could do little. But if the problem is primarily organizational, then the solution is also in the hands of the organization, and that opens up a concrete and achievable margin of action.

A fifth conclusion concerns the specific characteristics of the home environment as a care setting. Workers who provide care in patients' homes operate under conditions that differ substantially from those of hospital settings, including limited access to physically present colleagues, on-site infrastructure, without the opportunity of immediate referral to other healthcare services when clinical situations require it.

That operational solitude turns perceived institutional support into something more than a comfort variable; it becomes a critical resource for emotional sustenance. When that support is lacking or perceived as insufficient, the home care worker is exposed to a level of pressure that hospital staff face differently, precisely because they have an institutional cushion that the home care worker does not always have. This study highlights that difference and quantifies it.

Finally, this work makes a contribution that goes beyond its own data, establishing home care as a setting that deserves its own research in occupational health, not as an extension of the hospital but as a workplace with specific logics, vulnerabilities, and needs.

Most of the available evidence on SB in health has been built in hospital settings, and intervention policies often replicate that model. The findings presented here suggest that direct transplantation may be insufficient, and that understanding Burnout in home-based work requires research specifically designed for that context, with instruments sensitive to its particularities and intervention proposals that recognize that the place where the work occurs partly defines the conditions that sustain it or erode it.

# Limitations and future research

The cross-sectional design does not allow for establishing causal relationships; it can be stated that organizational climate and Burnout would significantly affect this sample, but not that one causes the other in a specific temporal sense.

It is possible that workers with higher levels of burnout perceive their organizational climate negatively, creating a bidirectional relationship that could only be unraveled with a longitudinal design.

Additionally, the study is confined to a single home care institution, which limits the transferability of the results to other organizational, regional, or cultural contexts. The sample is a census, which is a strength for internal validity, but it does not allow for generalization through classical statistical inference to other populations.

Finally, the exclusive use of self-report instruments introduces the risk of response bias and common method variance, which could artificially inflate the observed correlations. These considerations do not invalidate the findings, but they do define the future research agenda for this still underexplored field.