Methodological Insights Involving Subject Matter Experts in Community Research: An Example From a Black Older Adults Qualitative Research Study
Bibliographic record
Abstract
Background: Subject Matter Experts (SME) play a fundamental role in shaping community-based research. Engaging SMEs ( Phase 1 ) prior to community involvement ( Phase 2 ) is imperative for generative and fruitful research. Despite the extensive body of literature on community engagement and the important role of SMEs, this literature is often coupled with a lack of clarity behind step-by-step guides of what that process may look like. Aim: Through Phase 1 of The Needs Evaluation to Learn Valuable Information about Aging in Canada (NELVIA-Can) study, focusing on Black older adults in the Greater Toronto Area (GTA), we aim to discuss the salience and methods behind SMEs as a critical component of community-based research in informing study design and development. Methods: We conducted remote semi-structured interviews with SMEs where they gave input on study materials and procedures. SMEs included those with professional and personal working experiences with Black older adults (e.g., caregivers, academics, healthcare providers, and directors of community organizations). The interviews were recorded and transcribed. A content analysis was conducted with a three-member coding team to better understand the challenges and solution strategies that Black older adults experience, as well as recommendations for the study procedure and potential ways to ensure the study is culturally relevant. Results: Six methodologically insightful themes emerged from the data analysis, including (1) cultural context and cultural safety, (2) awareness of challenges that Black older adults face, (3) tailoring the research process to center the community’s diversity, (4) the critical role of trust and strategies for building trust, (5) uncovering research and healthcare gaps, and (6) intergenerational engagement in creating age-friendly communities. Conclusion: Findings will inform the design of a needs assessment for Black older adults in the GTA, as well as the development of technologies and policies that centralize Black older adults’ needs, preferences, and abilities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.102 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".