Application of a needs-driven, competencies-based mental health training program to a post-disaster situation: the Grenada experience.
Bibliographic record
Abstract
This report outlines an innovative approach to address post-natural disaster mental health needs in a region in which natural disasters are common---the Caribbean. Instead of traditional external vertical psychosocial interventions commonly used in this region, the authors developed and implemented a mental health interventions training program, in the island country of Grenada, which is focused on enhancing the capacity of local community-based health service providers to provide immediate and continued mental healthcare following a natural disaster. Soon after this training, a hurricane stuck the island of Grenada. A review of the self-confidence in the application of this training and the mental health intervention activities of these community health providers demonstrated that they felt able to effectively identify, intervene, and address post-disaster mental health needs within their communities and that their care of individuals affected continued beyond the immediate post-disaster period, suggesting that enhancing the capacity of local community-based health providers to deal with post-natural disaster mental health needs may be a useful model that may be applicable in other jurisdictions.
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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 | 0.003 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".