Perceptions of mobile and acute healthcare services among people experiencing homelessness
Bibliographic record
Abstract
OBJECTIVES: This paper presents findings from our collaborative research on the perceptions and preferences of people experiencing homelessness regarding outreach nursing services. METHOD: We conducted qualitative research using a critical ethnography approach. SAMPLE: A total of 15 participants were interviewed individually (n = 12 people experiencing homelessness) and in focus groups (n = 3 care providers). We also conducted direct observation. RESULTS: This paper focuses on one of the core categories that emerged from the data analysis "Perception of Health Care." This category emerged from the following three subcategories, which we will present in this paper: (1) Conflicting Relationships with Institutional Health Services; (2) Perception of Outreach Services; (3) Recommendations from Mobile Clinic Users. CONCLUSION: There are a range of perceptions of health services among people experiencing homelessness. Some are satisfied with the care received in the public health system, while many have experienced dehumanizing practices. Overall, outreach services are a promising strategy to reach people who are not served by the traditional modes of care delivery. Based on our findings, we suggest several key practices to personalize and adapt healthcare services and foster inclusive environments to better serve people experiencing homelessness.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".