Impact of a Brief Training on Mental Health Stakeholders’ Implementation of Evidence-Based Strategies for Trauma in the Caribbean Within the Context of COVID-19
Bibliographic record
Abstract
Abstract: Objective: Lack of governmental funding/infrastructure for mental health has created urgent need for effective training in evidence-based treatments for trauma in the Caribbean for under-resourced providers. Furthermore, impacts of the COVID-19 health crisis on providers’ implementation of skills in this region are currently unknown. Method: We examined impacts of a one-day training workshop for stakeholders in the Caribbean providing psychoeducation on trauma and PTSD and training in short-term interventions. Participants ( n = 46) were surveyed at preworkshop and postworkshop and at 3-month and 6-month follow-up (which coincided with the first 6 months of the COVID-19 pandemic) to assess retention of any changes in perceived knowledge about trauma/effective treatments and subsequent skill implementation. Results: Participants reported significant pre–post workshop increases in perceived knowledge (pre M = 31.61, post M = 44.63) about trauma and its effective treatments ( t[45] = −6.17, p < .001). This perceived knowledge was significantly maintained over time (3M M = 42.66, 6M M = 40.75). Furthermore, participants reported significant use of several of the strategies taught at the workshop at follow-up. Higher reported emotional distress related to the pandemic was associated with lower implementation at 6M, despite significant retention in perceived knowledge from the workshop ( B = −0.02, β = −1.55, p < .05). Conclusions: Brief trauma-focused trainings can be helpful for providers in under-resourced global settings with observable impact on implementation practices over time, but providers’ emotional distress stemming from public health crises can influence practice behaviors. Subsequent impacts on policy/budget allocations are discussed.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".