Stress and the interpretation of ambiguous faces in police officers
Bibliographic record
Abstract
Fast and accurate decision-making are central in police officers’ duties. The processing of information relevant to inform decisions can be biased by the state of mind of officers, notably in the presence of stressful conditions. We sought to examine the link between different sources of stress and the presence of an interpretation bias in a task presenting ambiguous faces. A sample of 234 Canadian police officers took part in an online study measuring the number of stressful life events and the level of occupational stress. Participants were assigned to a stress-induced group or a control group. The stress induction was a challenging arithmetic task and the control task was a non-challenging arithmetic task. Participants indicated if the facial expression of 60 ambiguous faces was ‘negative’ or ‘positive’. The dependent measure was the mean number of positive interpretations. Perceived stress level, measured on a visual analogue scale, collected throughout the task indicated that the induction was successful. We found no difference in interpretations resulting from the stress induction. We did however find a significant negative correlation between the perceived stress measures and the interpretation of the faces; higher levels of perceived stress were associated with less positive interpretations. The number of stressful life events and occupational stress level were not associated with face interpretation. Paperwork and fatigue were reported as the most stressful aspects of the job by the officers, consistent with what has been found in studies conducted with police services worldwide. This study also highlights the relevance of perceived stress in police officers.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".