Evaluating clinical outcomes of routinely delivered task-shared care for depression in rural Haiti
Bibliographic record
Abstract
Abstract Background There is a growing literature in support of the effectiveness of task-shared mental health interventions in resource-limited settings globally. However, despite evidence that effect sizes are greater in research studies than actual care, the literature is sparse on the impact of such interventions as delivered in routine care. In this paper, we examine the clinical outcomes of routine depression care in a task-shared mental health system established in rural Haiti by the international health care organization Partners In Health, in collaboration with the Haitian Ministry of Health, following the 2010 earthquake. Methods For patients seeking depression care betw|een January 2016 and December 2019, we conducted mixed-effects longitudinal regression to quantify the effect of depression visit dose on symptoms, incorporating interaction effects to examine the relationship between baseline severity and dose. Results 306 patients attended 2052 visits. Each visit was associated with an average reduction of 1.11 in depression score (range 0–39), controlling for sex, age, and days in treatment (95% CI −1.478 to −0.91; p < 0.001). Patients with more severe symptoms experienced greater improvement as a function of visits (p = 0.04). Psychotherapy was provided less frequently and medication more often than expected for patients with moderate symptoms. Conclusions Our findings support the potential positive impact of scaling up routine mental health services in low- and middle-income countries, despite greater than expected variability in service provision, as well as the importance of understanding potential barriers and facilitators to care as they occur in resource-limited settings.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".