Identifying and Prioritizing Barriers for Mitigation through Meaningful Community Engagement
Bibliographic record
Abstract
Canadian immigrant populations come from a number of ethno-geographical backgrounds. These populations exhibit differences in their culture and life practices related to disease and disease pre-disposition that influence how, when, and why individuals seek healthcare. A preliminary literature search found a paucity of research on the effect of culture on the use of healthcare and on effective intervention strategies for improving its use. It was especially noted that there was very little research where the perspective of the immigrant populations were taken into consideration. As identified in the Strategy for Patient-Oriented Research (SPOR) initiative, involvement of communities in health research is important for designing interventions that are successful. To begin addressing the above-mentioned gaps, as a first step, we are conducting an engagement initiative with the following interconnected objectives: (I) To establish a continuum of collaboration that engages academic researchers, immigrant community members (patients and their families), policy makers, and health care providers (including clinician scientists). (II) To develop a program of research with the immigrant community as a partner where they will meaningfully contribute to produce and prioritize community-driven research questions. (III) As an end result of this collaboration grant, we will shape research questions chosen collectively based on the priority identified by the immigrant community for further pursual where meaningful roles for all team members will be ensured, especially making sure about the active roles of the community. Our proposed activities are establishing an integrated research team where the capacity for meaningful immigrant community engagement will be ensured. This collaboration will allow stakeholders to effectively work together while aligning, connecting and coordinating diverse resources.
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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 | 0.054 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".