Engaging populations living with vulnerable conditions in community‐based research: A concept mapping approach to understanding positive practices
Bibliographic record
Abstract
Abstract The goal of this research is to identify positive practices used when conducting community‐based research with people living with vulnerable conditions. Community‐based research practitioners who participated in the research included 37 researchers, community partners, program planners, and government employees, working in health, human services, children and youth, and education sectors. Concept mapping, a participatory stakeholder‐driven process, was used to generate a framework of how community‐based research practitioners responded to the complex environments of people living with vulnerable conditions when conducting research. Respondents generated positive practices, determined relationships among practices, and rated practices on frequency of use and perceived effectiveness. This study revealed 7 clusters of positive practices: ethical practices, participant supports, social accountability, community involvement, language competence, financial compensation, and project management.
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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 | 0.100 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".