Client-Provider Relationships in a Community Health Clinic for People Experiencing Homelessness: A Critical Ethnography
Bibliographic record
Abstract
People who are experiencing homelessness have the highest rates of morbidity, and the lowest age of mortality in Canada. The importance of health care relationships in addressing the needs of people who are experiencing homelessness has been demonstrated. However, two gaps still exist in the literature. Firstly, the dialectical relationship between space/place and social relations has been neglected in the context of health care and homelessness. Secondly, there is a dearth of research on the role of formal and informal policies on the enactment of client-provider relationships in this context. Therefore, in this study three research questions are addressed: 1) How is ‘place’ experienced by clients and providers within a community health clinic for people who are experiencing homelessness? 2) How are client-provider relationships enacted within this contexts? 3) How do clinic-level and broader social and health policies shape relationships in this context? These questions are explored within a critical theoretical perspective, and utilizing a critical ethnographic methodology. Data were collected using multiple methods of document review, participant observation, in-depth interviews and focus groups. The participants included clients at a community health clinic, and all clinic service providers.\nFindings from this study focus on the power relations between clients and providers as they negotiate formal and informal policies to meet convergent and divergent needs to promote health. Similarly, clients and providers contested the space of the clinic to form their meanings of the place. This study has implications for individual health care practices, developing health promoting places, informing local policies, and advocating for the refinement of system policies. Health care providers must ensure that they are attuned to the structural factors that will enhance or limit their ability to practice optimally. As well, opportunities for clients to assume leadership positions need to be considered so that clinics will be responsive to client needs. In terms of system policies, health care providers need to continue to give time to political advocacy in order to refine systems to better serve clients. In conclusion, promoting health with people who are experiencing homelessness will take refinement both in personal practice and in local/systemic policies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| 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".