Virtually Prioritizing a Community's Needs: What Would Make it Easier for People who are Experiencing Homelessness to Manage Their Diabetes?
Bibliographic record
Abstract
Introduction: During the pandemic, a group of people with lived experience (co-researchers) was convened for a community-based participatory research (CBPR) project in Calgary, AB that aimed to explore and address barriers to managing diabetes while experiencing homelessness. The group met bi-weekly using a videoconferencing platform on internet-enabled tablets. Objectives: Our aim is to explain the process we undertook to virtually engage in priority setting to identify a research priority for the CBPR project. Methods: Co-researchers participated in 17 focus group discussions about barriers to managing diabetes while experiencing homelessness, following which they were asked to brainstorm responses to the question, “What would make it easier for people who are experiencing homelessness to manage their diabetes?” In subsequent meetings, the responses were grouped to form categories. From those, the group chose the priority using a modified nominal group process, which involved sequentially ranking, then rating the categories. Ranking involved picking 1st, 2nd, 3rd and 4th choices, and rating involved distributing 0 to 10 points amongst the categories. Results: Seven categories were formed: Healthcare; Screening for Diabetes; Housing and Shelter; Access to Medications and Supplies; Healthy Food; Diabetes Awareness; and Diabetes Education. Among these, Diabetes Awareness was given the most votes during the ranking and the most points during the rating exercises. Therefore, this is the topic our research will be focused on. Conclusion: We will conduct research for the purpose of increasing diabetes awareness, among shelter staff specifically, and use forum theatre and a short narrative film to share the findings.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".