Client–provider relationships in a community health clinic for people who are experiencing homelessness
Bibliographic record
Abstract
Recognizing the importance of health-promoting relationships in engaging people who are experiencing homelessness in care, most research on health clinics for homeless persons has involved some recognition of client-provider relationships. However, what has been lacking is the inclusion of a critical analysis of the policy context in which relationships are enacted. In this paper, we question how client-provider relationships are enacted within the culture of community care with people who are experiencing homelessness and how clinic-level and broader social and health policies shape relationships in this context. We explore these questions within a critical theoretical perspective utilizing a critical ethnographic methodology. Data were collected using multiple methods of document review, participant observation, in-depth interviews and focus groups. The participants include both clients at a community health clinic, and all clinic service providers. We explore how clients and providers characterized each other as 'good' or 'bad'. For providers, this served as a means by which they policed behaviours and enforced social norms. The means by which both providers' and clients' negotiated relationships are explored, but this is couched within both local and system-level policies. This study highlights the importance of healthcare providers and clients being involved in broader policy and systemic change.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".