Fishers’ attributed causes of accidents and implications for prevention education
Bibliographic record
Abstract
Commercial fishers are employed in one of the most dangerous jobs in Canada. Additionally, they tend both not to report work injuries and to deny and trivialize risks their job entails. This study focuses on fishers' subjective interpretation of their work environment. Its purposes were to examine fishers' attributed causes of accidents and to derive implications for prevention education. The researcher employed a qualitative methodology and interviewed 12 professional fishers who worked on the British Columbia coast. The interviews focused on fishers' descriptions of accidents and their attributed causes. Attribution theory was operationalized to provide a conceptual framework through which to analyze the 12 transcripts. The researcher transcribed the interviews, then highlighted and analyzed excerpts depicting the fishers' attributed causes of accidents. Three strategies were employed to examine the trustworthiness of the researcher's judgements regarding the transcripts and final interpretation of the data. The strategies were: use of a research partner (consistency), conducting a participant review (credibility), and comparison with another study (triangulation). The participants of this study attributed multiple causes to a given accident and their explanations were complex. The study found 22 categories of causes of accidents. The attributed causes from 9 of the 12 participants were distributed in all quadrants of attributions on the orienting framework (external/stable, external/unstable, internal/stable and internal/unstable). Five or more participants attributed the following as causes in their accidents: Economic Pressures, Luck or Fate, Weather Conditions Expected, Fatigue, and Stress. This study's results suggest that the techno-rational approach of existing traditional training programs, that concentrate on causes located mainly in the external/stable quadrant, does not concur with fishers' attributed causes of accidents. The study indicates that prevention education program content should be broadened to address the full spectrum of fishers' attributed causes of accidents. Through the utilization of fishers' attributed causes of accidents, prevention education programs could assist fishers to focus on their perceptions of occupational hazards and risks, and address questions of past risk taking and future risk assessment. From these insights fishers can review what can be done to control or eliminate a particular risk.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".