Generalized anxiety disorder and major depressive disorder among healthcare professionals in Mbarara city, southwestern Uganda: the relationship with professional quality of life and resilience
Bibliographic record
Abstract
BACKGROUND: Marking the lives of healthcare professionals (HCPs) are tensions arising from the conflict between fulfilling their duty of care and the demands of the healthcare setting, creating concern for HCPs' mental. In our study, we aim to determine the prevalence of major depressive disorder (MDD) and generalized anxiety disorder (GAD) among HCPs in Southwestern Uganda and to establish how the disorders' prevalence affects professional quality of life and resilience. METHOD: In total, 200 HCPs from two health facilities (one private and one public) in Southwestern Uganda enrolled in an analytical cross-sectional study. We used the Patient Health Questionnaire-9 (PHQ-9) to determine MDD, the Generalized Anxiety Disorder-7 (GAD-7) to determine GAD, the Professional Quality of Life Scale-5 (ProQOL-5) to determine professional quality of life and the Nicholson McBride Resilience Questionnaire (NMRQ) to determine resilience. RESULTS: The prevalence of MDD was 11.0% and of GAD was 14.5%. High compassion fatigue increased the likelihood of MDD [aPR = 3.38, p value < 0.001]. However, high compassion satisfaction and exceptional resilience reduced the likelihood of GAD i.e., [aPR = 0.50, p value < 0.001] and [aPR = 0.50, p value < 0.001] respectively. Being male [aPR = 2.41, p value = 0.005] and being married [aPR = 1.79, p value = 0.017] increased the likelihood of having MDD. The likelihood of GAD among HCPs decreased with age, [aPR = 0.97, p value = 0.022]. CONCLUSION: There is a significant occurrence of MDD and GAD among healthcare professionals (HCPs) in Southwestern Uganda. Compassion fatigue is linked to an elevated prevalence of MDD, while compassion satisfaction and high resilience scores are associated with a reduced prevalence of GAD. We recommend creation and execution of extensive mental health initiatives designed for HCPs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".