(P1-56) Recent Scientific Writing about Consequences of Disasters on the Health of Worker
Bibliographic record
Abstract
Recent Scientific Writings about Consequences of disasters on Workers Danielle Maltais, Ph.D. and Simon Gauthier, M.Sc. University of Quebec in Chicoutimi (UQAC) When an application of emergency measures is issued following a natural or technological disaster, or a disaster caused by human negligence, in many countries social workers and nurses play a central role in the support to the victims not only during the period of social disturbance but also at the time of the return to a normal life. These workers sometimes find themselves plunged within various intervention sectors where work conditions are often difficult. Once juxtaposed to the characteristics attached to disasters (nature, suddenness, duration, intensity, etc), the characteristics of the workers (intervention skills, training received, intrinsic efforts made, etc) and to the characteristics of the organizations (expectations towards their employees, organizational support offered to the employees, extrinsic efforts required, etc), these conditions increase their level of vulnerability by exposing them to environments harsh to manage. This vulnerability experienced by the workers in an emergency period can be reflected through symptoms such as anxious disorders and exhaustion. This poster will present the major findings of recent studies in this field (impact of disaster on the psychological health of workers) while under lighting personal, contextual and organizational factors which either contribute to the presence of psychological health issues for the workers or facilitate their resilience.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".