Clinic outcomes of the Pathway to Care Model: A cross-sectional survey of adolescent depression in Malawi
Bibliographic record
Abstract
BACKGROUND: Depression is one of the leading contributors to the global burden of disease and often has an onset during adolescence. While effective treatments are available, many low-income countries, such as Malawi, lack appropriately trained health providers in community health settings, and this limits access to effective mental healthcare for young people with depression. To address this need, a Canadian-developed youth depression Pathway to Care Model, linking school-based mental health literacy interventions to training of community healthcare providers, was adapted for use in Malawi and successfully applied. METHODS: A sample of healthcare providers (N = 25) from community health clinics (N = 9) were trained in the use of comprehensive, systematic clinical interventions, addressing the identification, diagnosis, and treatment of depression in youth who had been referred from schools where mental health literacy interventions had been implemented. Referral outcomes were obtained using a standardised clinical record form. RESULTS: Over 120 clinical outcome forms were available for analysis. Seventy percent of youth referred by their teachers were diagnosed with depression. Most youth diagnosed with depression identified physical symptoms as their primary difficulty. Available standardised outcome measures applied by clinicians indicated that, overall, youth showed positive outcomes as a result of treatment. CONCLUSIONS: Community healthcare providers in Malawi were trained in the identification, diagnosis, and treatment of youth depression. When this training was applied in usual clinical care to youth referred from schools, it led to generally favourable clinical outcomes. To our knowledge, this is the first demonstration of a clinically feasible intervention that results in positive outcomes for young people with depression in Malawi, and it may provide a useful model to replicate elsewhere in sub-Saharan Africa.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 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".