Scaling up interventions for depression in sub-Saharan Africa: lessons from Zimbabwe
Bibliographic record
Abstract
Background There is a dearth of information on how to scale-up evidence-based psychological interventions, particularly within the context of existing HIV programs. This paper describes a strategy for the scale-up of an intervention delivered by lay health workers (LHWs) to 60 primary health care facilities in Zimbabwe. Methods A mixed methods approach was utilized as follows: (1) needs assessment using a semi-structured questionnaire to obtain information from nurses (n = 48) and focus group discussions with District Health Promoters (n = 12) to identify key priority areas; (2) skills assessment to identify core competencies and current gaps of LHWs (n = 300) employed in the 60 clinics; (3) consultation workshops (n = 2) with key stakeholders to determine referral pathways; and (4) in-depth interviews and consultations to determine funding mechanisms for the scale-up. Results Five cross-cutting issues were identified as critical and needing to be addressed for a successful scale-up. These included: the lack of training in mental health, unavailability of psychiatric drugs, depleted clinical staff levels, unavailability of time for counseling, and poor and unreliable referral systems for people suffering with depression. Consensus was reached by stakeholders on supervision and support structure to address the cross-cutting issues described above and funding was successfully secured for the scale-up. Conclusion Key requirements for success included early buy-in from key stakeholders, extensive consultation at each point of the scale-up journey, financial support both locally and externally, and a coherent sustainability plan endorsed by both government and private sectors.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".