ASSESSING ALEXITHYMIA IN LAW ENFORCEMENT: THE ROLE OF SERVICE TENURE AND SEX
Bibliographic record
Abstract
ABSTRACT Alexithymia, characterized by difficulties in identifying and expressing emotions, has significant implications for professions requiring emotional intelligence and trauma-informed response. Law enforcement officers routinely navigate high-stress encounters that demand emotional regulation, clear communication, and empathy, yet those with alexithymic traits may struggle in these areas, potentially affecting interactions with victims, suspects, and the community. This study examined the prevalence of alexithymia among front-line police officers in suburban municipal agencies within the Chicago metropolitan area, using the Toronto Alexithymia Scale-20 (TAS-20) as a standardized assessment. The research addressed three questions: (1) What proportion of officers exhibited normative or elevated levels of alexithymia? (2) Did alexithymia vary by years of service? (3) Were there differences between male and female officers? A quantitative methodology was employed, collecting survey data from 44 officers categorized by sex and tenure, distinguishing those with less than 5 years from those with 5 or more. Findings indicated that while most officers were non-alexithymic, a notable subset exhibited normative or severe alexithymia, raising concerns about emotional processing within the profession. Statistical analysis revealed no statistically significant differences based on sex or years of service, suggesting alexithymic tendencies may be more individually variable than demographically linked. These findings highlight the need for trauma-informed policing strategies that account for emotional processing deficits. Enhancing officers’ ability to recognize and regulate emotions may improve victim interactions, crisis de-escalation, and community trust, reinforcing the importance of integrating emotional intelligence training within trauma-informed law enforcement practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".