Addressing the need for Indigenous‐specific PROMs and PREMS: A focus on methodology
Bibliographic record
Abstract
PURPOSE: Differences in Indigenous worldviews, practices and values highlight the need for Indigenous-specific health quality indicators, such as patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs). The purpose of this paper is to present our methodology, as part of a larger study that sought to develop a framework for creating Indigenous-specific PROMs and PREMs. METHODS: The research design was informed by Indigenous research methodology and a community-based participatory approach. It had three core components: (1) a literature exploration of existing Indigenous-specific PROMs and PREMs; (2) interviews with researchers with expertise in PROMs and PREMs developed for Indigenous populations and community leaders interested in using these Indigenous-informed evaluation tools; and (3) conversations with Indigenous community members about their experiences with health surveys. Interviews were audio-recorded and transcribed verbatim; transcripts were analyzed qualitatively using an inductive and deductive approach. Themes and sub-themes were identified to build a framework that honours Indigenous knowledges and ways of knowing. Results were validated with select research participants and the Project Advisory Committee. RESULTS: Findings demonstrate how relationship building is the necessary starting point for engagement when developing survey instruments with Indigenous peoples. Engagement requires respectful collaboration through all stages of the project from determining what questions are asked to how the information will be collected, interpreted, and managed. A relational stance requires responsibility to Indigenous communities and peoples that goes beyond research carried out using a western scientific lens. It means ensuring that the project is beneficial to the community and framing questions based on Indigenous knowledge, worldviews, and community involvement. CONCLUSIONS: This study employed a collaborative, participatory qualitative approach to develop a framework for creating PROMs and PREMs with Indigenous peoples. The methods described offer concrete examples of strategies that can be employed to support relationship-building and collaboration when developing Indigenous-specific survey instruments.
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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 | 0.345 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".