Pathways: A guide for developing culturally safe and appropriate patient‐reported outcome (PROMs) and experience measures (PREMs) with Indigenous peoples
Bibliographic record
Abstract
BACKGROUND: Members of the Indigenous Health Program, BC Children's and Women's Hospitals and the University of British Columbia embarked on a joint project to describe best practices to support the creation of patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) with Indigenous peoples. METHODS: The project involved a review of previous research on patient-reported measures (surveys) that had been specifically developed for Indigenous populations. It also involved interviews with key stakeholders-Indigenous and non-Indigenous academic researchers, and Indigenous community leaders and community members. Themes from the interviews and the literature review were combined and synthesized into pathways/a framework for survey development. RESULTS: The pathways document consisted of 13 protocols and associated teachings for guiding processes and framing survey questions. These encompassed building relationships, community engagement and consultation, benefits to community, ceremony and storytelling, two-way learning, participatory content development, governance and accountability. Findings emphasized the criticality of Indigenous leadership in setting priorities for PROMs and PREMS and establishing relationships that honour Indigenous experiences through all phases of a study. Assessment of the framework's validity with select research participants and the Project Advisory Committee was positive. CONCLUSION: This is the first framework to guide development of PROMs and PREMs with Indigenous peoples and communities. It addresess both process and outcome and includes concrete steps that collaborators can take when establishing a partnership that is respectful and inclusive of Indigenous ways of knowing and being.
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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 | 0.080 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".