A role identity perspective on paramedic mental health
Bibliographic record
Abstract
Introduction Role identity theory explains that people derive a sense of purpose and meaning from holding social roles, which, in turn, is linked with health and well-being. Paramedics have a respected role in society but high rates of mental illness. I used role identity theory to explore what might be contributing to poor mental health among paramedics. Objectives My objectives were to estimate the prevalence of Post-Traumatic Stress Disorder (PTSD), depression, and anxiety; assess for relationships with a measure of paramedic role identity; and finally, explore how role identity conflict could lead to distress. Methods I used a mixed methods approach situated in a single paramedic service in Ontario, Canada, distributing a cross-sectional survey during the fall 2019/winter 2020 Continuing Medical Education (CMEs) sessions while also interviewing a purposively selected sample of 21 paramedics. The survey contained a demographic questionnaire, a battery of self-report measures, and an existing paramedic role identity scale. Each interview was transcribed verbatim and analyzed thematically with role identity theory as a conceptual framework. Results In total, 589 paramedics completed the survey (97% of CME attendees), with 11% screening positive for PTSD, 15% for major depressive disorder, 15% for generalized anxiety disorder, and 25% for any of the three. Full-time employees, women, those with ‘low’ self-reported resilience, and current or former members of the peer support team were more likely to screen positive. The dimensions of paramedic role identity were not associated with an increased risk; however, I defined a framework through the interviews wherein chronic, identity-relevant disruptive events contribute to psychological distress and disability. Conclusions Our prevalence estimates were lower than have been previously reported but point to a mental health crisis within the profession. Role identity theory provided a useful framework through which to reconceptualize stressors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".