Identifying research priorities for pregnant South Asian immigrants in Canada: A James Lind Alliance approach
Bibliographic record
Abstract
BACKGROUND: Pregnant South Asian immigrants (PSAI) make up a significant portion of immigrants to Canada and face a higher risk of adverse pregnancy outcomes compared to other ethnic groups. This disparity is largely due to cultural and linguistic barriers to maternity care, including language difficulties, limited cultural sensitivity in healthcare services, and a lack of awareness about culturally tailored educational resources. Despite the growing number of PSAI in Canada, there is limited understanding of how to best support their healthcare and well-being. To address this gap, we aim to conduct a priority-setting exercise to identify key research priorities and establish a patient-oriented research agenda that will drive long-term, impactful research and ultimately improve maternal health outcomes for PSAI in Canada. METHODS: This project follows the James Lind Alliance (JLA) priority-setting partnership (PSP) methodology, which includes forming a steering committee, identifying and verifying uncertainties, conducting an interim priority-setting phase, and holding a final workshop. Participants will include first-generation South Asian immigrant women from Bangladesh, India, Pakistan, and Sri Lanka, as well as clinicians, researchers, and community/professional organizations from Ontario, Alberta, and British Columbia. Data will be collected through Zoom-based recorded working group sessions and an online ranking survey. Qualitative data will be analyzed using an inductive content analysis approach supported by NVivo software. Subgroup diversity (e.g., ethnicity, gender, age, and geography) will be tracked across participant groups. Consensus on top research priorities will be achieved through a structured ranking process followed by a facilitated virtual workshop. The study began in May 2025 and is expected to conclude by January 2026, a timeline consistent with similar JLA PSP initiatives. DISSEMINATION: All findings will be shared through a peer-reviewed publication and conference presentations for the scientific community, a lay summary for community organizations, and a video and infographic for patient participants. Community and professional organizations will also support the dissemination of findings through their networks and social media channels.
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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 | 0.121 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.044 | 0.015 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.005 | 0.009 |
| 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; 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".