Quality of life of depressed and suicidal patients seeking services from traditional and faith healers in rural Kenya
Bibliographic record
Abstract
BACKGROUND: In rural Kenya, traditional and faith healers provide an alternative pathway to health care, including mental health care. However, not much is known about the characteristics of the populations they serve. The purpose of this study was to determine the relationship between depression, suicidal ideation, and socio-demographic variables with Quality of Life (QoL) indicators in a sample seeking mental health services from traditional and faith healers in rural Kenya. Understanding QoL in this sample can help develop mental health policy and training to improve the well-being of this population. METHOD: This was a cross-sectional epidemiological survey (n = 443) conducted over a period of 3 months among adult patients seeking care from traditional and faith healers in rural Kenya. Data were collected using the Beck Depression Inventory II (BDI-II), Beck Scale for Suicide Ideation (BSS) and WHO Quality of Life Survey- BREF (WHOQOL-BREF), and analyzed using correlation analyses, parametric tests, and regression analyses. RESULTS: Increasing levels of depression were associated with lower QoL among patients seeking care from traditional and faith healers. BSS scores were significantly negatively correlated with overall, physical, psychological, and environmental QoL, p < .05. There was a statistically significant difference between mean scores for overall QoL between depressed (M = 2.35, SD = 0.76) and non-depressed participants (M = 3.03, SD = 0.67), t(441) = 8.899, p < .001. Overall life satisfaction for depressed participants (M = 2.23, SD = 0.69) was significantly lower than non-depressed participants. Regression analyses indicated that depression, suicidal ideation, and being married predicted lower overall QoL controlling for other variables. Post hoc tests and subgroup analysis by gender revealed significant differences for females only. Depression, and older age predicted lower life satisfaction whereas being self-employed predicted higher life satisfaction, when controlling for other variables. CONCLUSION: This study sheds light on correlates of QoL in depressed and non-depressed patients in rural Kenya. Evidence suggests that traditional and faith healers treat patients with a variety of QoL issues. Further research should focus on understanding how these issues tie into QoL, and how these healers can target these to improve care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".