The Pre-employment Clinical Assessment of Police Candidates: Principles and Guidelines for Canadian Psychologists
Bibliographic record
Abstract
Identification and selection of acceptable police candidates is a complex and multi-step process. The Canadian Police Sector Council has articulated a comprehensive process by which appropriate candidates can be identified, and candidates who are likely to be unsatisfactory police officers can be eliminated from consideration (see A Guide to Constable Selection: A Best practice Approach and Research Update (July, 2011, hereafter to referred to as the PSC Guide). The current Guidelines describe one aspect of the selection process outlined in the PSC Guide, the clinical psychological assessment. The purpose of the clinical assessment is primarily to identify candidates who exhibit personality traits, behaviour patterns or psychological characteristics that are likely to be problematic in the police workplace. The clinical assessment is intended to contribute information to be considered in the overall assessment of suitability, but it is not a selection decision in and of itself. As is stipulated in the PSC Guide, there are a number of ways in which psychologists and other specialists with expertise in employment selection and measurement can be involved in the pre-employment selection and assessment of police candidates. At the broadest level, they may play an integral role in the overall development of a selection process. They may be involved in
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".