Storytelling of Indigenous patient and family advocates engaged in patient-oriented research initiatives in the field of inflammatory bowel disease
Bibliographic record
Abstract
Background and aim: The history of colonization and its ongoing impact poses significant health disparities among Indigenous communities. We aimed to centre the voices and stories of Indigenous patients and family advocates (IPFAs-Indigenous patients living with inflammatory bowel disease [IBD] and family members of Indigenous individuals with IBD) engaged in patient-oriented research projects and who are part of the IBD among Indigenous Peoples Research Team (IBD-IPRT). Methods: IPFAs and Indigenous and non-Indigenous researchers of the IBD-IPRT followed a storytelling research methodology to let IPFAs share their stories as research team members. Four IPFAs documented their experiences as IBD patients, advocates, and research partners. The stories were analyzed for themes. The identified themes were collaboratively verified with the IPFAs. Results: The full stories shared by the IPFAs were transcribed and presented in this paper. Following a background analysis of themes in the 4 narratives, we were also able to identify 4 key themes that could be relevant to improving patient-oriented research initiatives: (1) health promotion, (2) leadership and voice, (3) community engagement, and (4) disease awareness and access to care. Trust building, strong relationships, and effective partnerships are core components for conducting patient-oriented research with Indigenous community members. Conclusions: Indigenous patient engagement in health research is crucial to ensure that lived experiences, knowledge, and cultural values are adequately adopted to improve research outcomes. Centering IPFAs in IBD research can promote cultural awareness and actionable recommendations to improve health outcomes for individuals with IBD and their families and caregivers.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".