Applicability of a national strategy for patient-oriented research to people who use(d) substances: a Canadian experience
Bibliographic record
Abstract
BACKGROUND: Europe and North America are in the grips of a devastating overdose crisis. People who use substances often feel unsafe to access healthcare due to fears of stigma, blame, judgement, poor treatment, or other repercussions. As a result, they often avoid, delay, or leave care, resulting in premature death and missed opportunities for care. Internationally, there have been concerted efforts to move towards patient-engaged research to enhance the quality of health care systems and services. In Canada, the Canadian Institutes of Health Research (CIHR) Strategy for Patient-Oriented Research (SPOR) initiative promotes engagement of patients as active partners in health care research. As part of a community based patient oriented research project, we critically analyze the SPOR framework to provide insights into what constitutes safer research with people who use(d) substances. METHODS: We undertook a two-stage process that began with a review of community based research principles and the SPOR framework. At the second stage, we undertook a qualitative descriptive study employing focus groups to generate description of the adequacy and appropriateness of the SPOR framework for guiding research with people who use(d) substances on four key dimensions (patient engagement, guiding principles, core areas of engagement and benefits). The data were analyzed using qualitative content analysis to identify key issues and insights. RESULTS: While the SPOR framework includes a range of patient roles, principles and areas for engagement, there are issues and gaps related to essential elements of safe patient-oriented research for people who use substances. These include an individualized focus on patients as partners, lack of recognition of community benefits, power imbalances and distrust due to systemic stigma, engagement as one way capacity building and learning, and lack of accountability for taking action on research findings. CONCLUSIONS: Given the extent of stigma in health care and the ongoing illicit drug policy crisis, strategies for enhancing equitable Patient-Oriented Research (POR) include shifting language from patient partners to community researchers, recognizing power inequities and adding trust and equity as core POR principles including pay equity. Employing community based participatory research as a POR methodology allows the lead researchers to fully engage community throughout the research process, enhances community benefits and accountability for action.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".