Preparing social work students for the realities of practice: A content analysis of occupational stress injury in social work curricula in Canada
Bibliographic record
Abstract
The practice of social work places social workers at risk for occupational stress injuries (OSIs) such as 1) post-traumatic stress disorder (PTSD), 2) secondary traumatic stress (STS), also known as vicarious trauma (VT) or compassion fatigue (CF), and 3) burnout (Bride, 2007; Shackelford, 2012). Therefore, schools of social work need to prepare students for these potentialities and teach them ways to mitigate risk. This study explores whether and how thoroughly schools of social work across Canada are including the topic of OSIs within their Bachelor of Social Work (BSW) curricula. A content analysis was conducted of course descriptions, from each of the 42 BSW programs across Canada, using online university calendars and program websites. Each course description was reviewed for explicit reference to OSIs, (e.g., use of the words PTSD, STS, VT, CF and burnout) and also for implicit mention to the psychological impacts of practicing social work on the social workers themselves. Only three BSW programs offered a course with explicit reference to OSIs, an additional 11 offered courses with implicit reference. Of the 1,494 course descriptions included in the sample, only four explicitly referenced OSIs and 23 made implicit mention to topics that could arguably align with the etiology of OSIs and prevention and mitigation strategies. These topics included worker stress, personal and professional sustainability, professional resiliency, and self-care. These findings raise concern as to how well social work students are being prepared for the realities of practice. Implications of the findings for social work education and preparation for the field are discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".