Color Coded Health Data: Factors related to willingness to share health information in South Asians
Bibliographic record
Abstract
Background: Canada is becoming an increasing multicultural society welcoming individuals of various ethnicities. Ethnicity has become an established modifier of health in Canada, where ethnocultural communities face health disparities for multiple health outcomes. To understand these health disparities further, a call for high quality health data for ethnocultural communities has been made. Since health information availability is controlled by the participant, it is important to understand the willingness to share health information by an ethnic population to increase data availability within ethnocultural communities. Objectives: The objectives of this study aimed to explore and synthesize factors associated with willingness to share health information via a rapid review of literature and qualitative interviews with (South Asian) SA participants, the largest ethnic group in Canada. Findings: Triangulating results from both the rapid review of literature and the qualitative interviews, revealed that factors associated with sharing health information operated at 3 different levels: 1) community level, 2) individual level, and 3) process level. These factors also operated through a lens that considered the cultural and sociodemographic aspect of ethnocultural communities. Conclusions: The results of this study reveal important factors associated with sharing health information for ethnocultural communities, and support the need for culturally sensitive and respectful engagement with the community, ethically sound research practices that make participants feel comfortable to share their information, and an easy and incentivised process to share their information feasibly. Future study should aim to understand and measure data-sharing partnerships between researchers and ethnocultural communities to maximize data availability for ethnic populations.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".