When I can’t help, I suffer: A scoping review of moral distress in service providers working with persons experiencing homelessness
Bibliographic record
Abstract
BACKGROUND: Service providers are experiencing mental health decline as they work to meet the needs of persons experiencing homelessness in a system that constrains their ability to help. Although moral distress is widely recognized in health care, the experience of moral distress in service providers working with people experiencing homelessness has not been explored in a scoping review. AIM: To identify the range and nature of literature on moral distress among service providers working with persons experiencing homelessness. METHODS: We conducted a scoping review using Arksey and O'Malley five-stage framework. RESULTS: From the 2219 records yielded from our search, 40 studies were included in this review. Our narrative synthesis generated three distinct themes: 1) helping is part of our identity, it's who we are, 2) we are doing the best we can, but there are so many barriers, 3) it's more than we can take, we're not okay. CONCLUSION: Service providers across studies were described as experiencing a high degree of moral distress in relation to constraints that impeded their ability to fulfil their moral value of helping.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".