Narratives by health professionals on solvent use and housing insecurity
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how service professionals involved in the provision of services to clientele who use solvents and are often without stable housing understand the process of healing and recovery in their work. Design/methodology/approach – Using a narrative methodology, semi-structured interviews were conducted with 12 human service professionals (i.e. social workers, case managers, etc.) employed in providing recovery-based services to individuals who use volatile solvents. Findings – Despite the dominant cultural story about “street addicts” and solvent users’ limited possibilities for recovery, professionals indicate that they view their clients as “just like everyone else.” The dominant storyline was that of advocating for the capability of the client group. These stories are discussed in relation to hope for professionals who provide health and housing services to clientele with complex and multi-systemic needs. Originality/value – The findings have implications for how human service providers maintain hope and purpose in their work with stigmatized populations (e.g. homeless individuals, those with alcohol or other drug-related problems). This study highlights how human service professionals make sense of their role in their work and how they maintain hope for themselves and for the recovery of the clientele they work alongside.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".