Training police search and rescue teams: Implications for missing persons work
Bibliographic record
Abstract
Police search and rescue teams are crucial players in resolving missing person cases. Resultantly, police employ a host of training for search and rescue members in collaboration with institutions, organizations, and groups. Such training, however, has not been studied. This warrants attention as, in a time of police legitimacy crises and austerity policing, appropriate and quality police training for effective, efficient practices is imperative. Therefore, we examined the training needs and offerings for police search and rescue personnel, and their impact on search and rescue operations and work, through thematic analysis of interviews with 52 police search and rescue members from 17 agencies across Canada. Findings suggest there are no homogeneous, structured, or standardized training offerings for police search and rescue personnel. Instead, training varies within and across agencies and regions, and between officers and roles, as it is commonly based upon anecdotal experiences and in-house developed “best practices.” We discuss the implications of these findings for police search and rescue operations and work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".