Exploring the Health Research Priorities of the South Asian Community in the Fraser Valley
Bibliographic record
Abstract
Introduction: Under-representation of ethnic minorities in research is a challenge for researchers in Canada. Barriers to participation of the South Asian (SA) community in research include lack of interest or connection to research, stigma, misunderstanding about u201cwhat is researchu201d and language skills1. In 2018, a collaborative of researchers, decision-makers, patients and front line health providers came together to improve engagement in research with the SA community in British Columbia (BC).Methods: This project used the u201cJames Lind Alliance Priority-Setting Partnership2u201d approach to explore the health research priorities of the SA community in Surrey and Abbotsford, BC4. Partnerships were established with community groups/organisations in the SA community. Surveys were conducted online and in-person in English/Punjabi/Hindi at temples, community centres, and health exhibitions. The survey was developed collaboratively with patient and community partners and collected data were thematically grouped to identify health research topics.Results:From April to June 2019, 198 people were surveyed; 43% males and 57% females. 62% of participants were between 18 and 60 years; 38% were 60+. Most participants were of Indian ethnicity (85%). A total of 597 health research questions were collected. The five most common topics were: 1) Diet; 2) Exercise; 3) Diabetes; 4) Complementary/Alternative Medicine; 5) Mental Health. Discussion/Implications:The engagement of patient partners, community groups and organizations was essential to this priority setting initiative. The findings will help shape a collaborative research agenda with the SA community to build capacity, engagement, and relevant research. In October 2019, health care providers and community partners will rank and generate a top 10 list of health research priorities for the SA community.Dissemination plan:Identified gaps in health knowledge will be shared with relevant Fraser Health departments. A peer-reviewed journal article is under development, and results will be shared with the SA community through workshops, summary documents, and presentations.Acknowledgement: We would like to acknowledge the effort of all our volunteers and community partners. This project was funded by the Surrey Hospital Foundation.References:1.tQuay TA, Frimer L, Janssen PA et al. Barriers and facilitators to recruitment of South Asians to health research: a scoping review. BMJ Open 2017;7;e014889. doi:10.1136/bmjopen-2016-0148892.tJames Lind Alliance (2018). The James Lind Alliance Guidebook Version 8. National Institute for Health Research
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".