Supports for Health and Social Service Providers from Canada Responding to the Disaster in Haiti
Bibliographic record
Abstract
UNLABELLED: In January 12, 2010, a 7.0 magnitude earthquake shook Port-au-Prince, Haiti. The massive disaster made it difficult for local Haitian community officials to respond immediately, leaving the country reliant on foreign aid and international and non-governmental relief organizations. This study explores the effectiveness of various supports that were made available to health and social service providers in Haiti, by focusing on their lived experiences pre-deployment, on-site and post-deployment. The paper provides a qualitative exploration of participant perceptions with respect to the success of their performance in response, and relevant literature describing the various supports provided to health and social service providers responding to disasters. METHODS: A single, semi-structured interview was conducted with Canadian health professionals (n=21) who deployed to Haiti during the time of, or after, the 2010 earthquake. The study uses Strauss and Corbin's structured approach to grounded theory to identify main themes and relationships in the interviews. RESULTS: The interviews indicate that training, and psychological and emotional supports for health and social service providers require improvement to enhance the experience and effectiveness of their work. CONCLUSIONS: Findings indicate that supports are most effective when they are tailored to the volunteers. The paper highlights future research stemming from the grounded theory findings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".