Gaining Insights from Subject Matter Experts to Develop a Racially and Culturally Appropriate Study in the Greater Toronto Area
Bibliographic record
Abstract
This study aims to develop a culturally relevant and inclusive needs assessment to inform the design of tailored technology solutions for Black older adults. Semi-structured, individual interviews were conducted with four Subject Matter Experts (SMEs) to ensure the larger study was tailored to the population's needs, including informing recruitment strategies. A three-member coding team conducted a content analysis on the interview transcriptions focusing on the research objectives. The findings provide valuable insights into best practices for conducting racially and culturally appropriate research and identify the everyday challenges Black older adults face in the Greater Toronto Area (GTA). Moreover, the findings underscore the significance of considering social determinants in study development and understanding the challenges. The discussions with the SMEs exemplify the importance of this methodology to ensure the study is relevant to the community in which they are situated. This study lays the groundwork for a deeper understanding of the challenges Black older adults in the GTA face and the potential solutions to address them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".