Insights from the front-line: social workers working with clients with mental health and substance use issues
Bibliographic record
Abstract
For social workers that work with clients who have Mental Health and Substance Use (MHSU) issues, this profession can be both very rewarding and very challenging. This paper examines the experiences of front-line social workers in a Victoria, B.C., as these experiences relate to working with clients with mental health and substance use issues. The role of Anti-Oppressive Social Work theories and practices relevant to MHSU, and how they influence field-work, will also be examined. For the purposes of this project, social workers were asked to participate in both verbal and written interviews. As a result of the interviews, several common themes emerged, including the stigma against mental health and substance use clients, Harm Reduction strategies currently in use, and the importance of working from a Housing First philosophy. This research provides valuable first-hand knowledge for social workers, other professionals who work with MHSU clients, and the community at large.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".