Social and cultural considerations in engaging visible minorities in physical activity research
Bibliographic record
Abstract
Introduction: Patient/public involvement in research is a concept that is increasingly becoming central to health research policy. Involvement of visible minority immigrants in research, who, very often are also at a higher risk of chronic diseases, has been historically low. Complex interactions between genetic predispositions, social stressors and life-style behaviors like diet and low physical activity have been implicated. Involvement of ethnic minorities in research helps give a voice to their opinions and guides meaningful research and is imperative for developing effective public health interventions.Objectives: a)Characterize how physical activity is culturally perceived, and adopted by people from three diverse ethnic groups (South Asian, Chinese, Africans); b)Identify unique factors that promote or inhibit involvement of ethnic minorities as partners in physical activity related research or health research in general.Methods: We held conversations and discussions with 16 community members and key informants from three ethnic groups. Discussions were recorded, transcribed and emerging themes were identified and classified into various categories.Findings: Difficulties integrating into the Canadian system, social/financial stressors, racial discrimination, absence of diversity in leadership positions, mistrust of the establishment, and factors like being alien to the culture of organized physical activity were some of the reasons for low participation of ethnic minorities in research.Conclusion: Creating a culture of ethnic minority involvement and participation in research is not an overnight process but takes years to evolve and requires a genuine, concerted effort on part of the researchers to develop long lasting relationships with ethnic communities based on mutual trust and respect.
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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 | 0.040 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".