Research Partnerships with Patients Living with Type 2 Diabetes: Practices and Challenges in Quebec Among People New to Canada
Bibliographic record
Abstract
Patients are increasingly encouraged to participate in health research programs as partners, with the aim to ensure that studies address their priorities. In response, the Strategy for Patient-Oriented Research (SPOR) has been created in Canada to transform the patient's role in research from a passive beneficiary to a more proactive partner of change within the healthcare system. This research investigates what people new to Canada living with type 2 diabetes think about participating in research partnerships. Using an ethnographic approach, 31 people new to Canada with a diagnosis of type 2 diabetes were interviewed. Findings indicated that few people new to Canada were represented among the Diabetes Action Canada (DAC) Network's Circles of Patient Partners in Quebec. Barriers to engagement in research were: lack of information; competing priorities; language barrier and privacy concerns; preconceptions about being a patient partner; prejudices on research engagement as something demanding and binding; and the matter of religious and gender differences. Some participants questioned the extent to which involvement in research can really meet their expectations considering institutional control over research, funding requirements that often dictate priorities and the biomedical approach which still, in many respects, dominates health research. Implications for achieving equity, diversity, and inclusion of patient partners in research are discussed.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".