“Systems trauma”: A qualitative study of work-related distress among service providers to people experiencing homelessness in Canada
Bibliographic record
Abstract
Service provision to people experiencing homelessness is challenging and stressful work. Yet, there is a dearth of evidence on how the work experiences of service providers contribute to mental health distress and wellness. This qualitative study examined the contributing factors to workplace mental health among service providers to people experiencing homelessness in Canada, with the aim of establishing a causal theory for how work-related challenges cause distress. In-depth interviews were conducted with 40 service providers working in the homeless service, supportive housing, and harm reduction sectors across Canada. Data were analyzed using a grounded theory-informed approach. The workplace mental health of service providers was centred on the concept of “systems trauma,” which refers to the structural and systemic factors that exacerbate the difficulty of service providers’ work, putting them at-risk of work-related mental health distress. “Systems trauma” had multifaceted causes and consequences, as did the organizational- and individual-level factors that protected service providers against its impacts. Overall, the findings highlight how the same sociopolitical context that negatively affects people experiencing homelessness also shapes the workplace mental health of service providers. Supports for managing moral distress, policy and public initiatives to improve the valuation of work with people experiencing homelessness, dedicated funding for workforce development, and further investment in primary and secondary prevention of homelessness are recommended to promote workplace mental health.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".