Utilizing a church-based platform for mental health interventions: exploring the role of the clergy and the treatment preference of women with depression
Bibliographic record
Abstract
Abstract Background Training lay people to deliver mental health interventions in the community can be an effective strategy to mitigate mental health manpower shortages in low- and middle-income countries. The healthy beginning initiative (HBI) is a congregation-based platform that uses this approach to train church-based lay health advisors to conduct mental health screening in community churches and link people to care. This paper explores the potential for a clergy-delivered therapy for mental disorders on the HBI platform and identifies the treatment preferences of women diagnosed with depression. Methods We conducted focus group discussion and free-listing exercise with 13 catholic clergy in churches that participated in HBI in Enugu, Nigeria. These exercises, guided by the positive, existential, or negative ( PEN-3 ) cultural model, explored their role in HBI, their beliefs about mental disorders, and their willingness to be trained to deliver therapy for mental disorders. We surveyed women diagnosed with depression in the same environment to understand their health-seeking behavior and treatment preferences. The development of the survey was guided by the health belief model . Results The clergy valued their role in HBI, expressed understanding of the bio-psycho-socio-spiritual model of mental disorders, and were willing to be trained to provide therapy for depression. Majority of the women surveyed preferred to receive therapy from trained clergy (92.9%), followed by a psychiatrist (89.3%), and psychologist (85.7%). Conclusion These findings support a potential clergy-focused, faith-informed adaptation of therapy for common mental disorders anchored in community churches to increase access to treatment in a resource-limited setting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".