Everyone goes home: Exploring the implicit learning of critical incidents in the volunteer fire service
Bibliographic record
Abstract
Volunteer firefighters make up approximately 85% of the fire service in Canada (Haynes, 2016) and almost the entire fire service on Prince Edward Island. As a result of this voluntary service, many firefighters are exposed to traumatic events and critical incidents, which may lead to poor mental health outcomes. Training and education in the area of critical incidents was shown to be lacking in a sample of 100 volunteer firefighters on PEI, with 67.6% indicating they have never been educated on mental health issues in the fire service (Brazil, 2017). Through a social constructionist theoretical lens, this study describes how volunteer firefighters informally learn and socially construct critical incidents and how to manage them. The findings suggest that there is a traditional culture into which firefighters are socialized and that the cultural values and norms that are implicitly learned lend to the informal learning pertaining to critical incidents. What is most notable about this study is that the traditional hyper-masculine fire service culture was found to be evolving, including the social constructs that contribute to its institutionalization. As such, firefighters are undergoing an informal re-education pertaining to critical incidents and mental health, which bodes well for the introduction of psychoeducation within the fire service.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".