Defining culturally safe primary care for people who use substances: a participatory concept mapping study
Bibliographic record
Abstract
BACKGROUND: People who use substances experience high levels of substance-related stigma, both within and outside of health care settings, which can prevent people from help-seeking and contribute further to health inequities. Recognizing and respecting how political, social, economic, and historical conditions influence health and health care, cultural safety, with origins in addressing Indigenous racism, is a potential strategy for mitigating stigma and marginalization in health care. Using a participatory research approach, we applied the concept of cultural safety to develop a model of safe primary care from the perspective of people who use substances. METHODS: People who use or used substances were involved in all phases of the research and led data collection. Study participants (n = 75) were 42.5 years old on average; half identified as female and one quarter as Indigenous. All were currently using or had previous experience with substances (alcohol and/or other drugs) and were recruited through two local peer-run support agencies. Concept mapping with hierarchical cluster analysis was used to develop the model of safe primary care, with data collected over three rounds of focus groups. RESULTS: Participants identified 73 unique statements to complete the focus prompt: "I would feel safe going to the doctor if …" The final model consisted of 8 clusters that cover a wide range of topics, from being treated with respect and not being red-flagged for substance use, to preserving confidentiality, advocacy for good care and systems change, and appropriate accommodations for anxiety and the effects of poverty and criminalization. CONCLUSIONS: Developing a definition of safe care (from the patient perspective) is the necessary first step in creating space for positive interactions and, in turn, improve care processes. This model provides numerous concrete suggestions for providers, as well as serving as starting point for the development of interventions designed to foster system change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".