Evaluating Perceptions of the Effectiveness of the Community Advisory Panel Model for Enhancing Service Delivery to Marginalized Populations
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate perceptions of the effectiveness of the community advisory panels (CAPs) at St. Michael's Hospital, in Toronto, Canada. DESIGN AND SAMPLE: A qualitative design was employed. Participants included hospital staff, patients, Community Advisory Panel chairs, and key informants from community services in the St. Michael's Hospital catchment area. MEASURES: An online survey about awareness of the CAPs and CAP accomplishments; (2) Key informants interviews; and (3) Review of memos and meeting minutes of the CAPs to assess their impact in the hospital and the community. St. Michael's Hospital was the setting of the study. Descriptive statistics were generated for the survey data. Qualitative interview data were coded for major themes. Participants included hospital staff, patients, CAP chairs, and key informants from community services in the St. Michael's Hospital catchment area. RESULTS: Although the CAPs initiated and implemented an array of programs and services at St. Michael's Hospital, the visibility of the CAPs and their service to the hospital and community were very low. Themes that emerged from the semistructured interviews involved the visibility, effectiveness, and role of the CAPs in the hospital. CONCLUSIONS: Although the CAPs at St. Michael's Hospital appear to be an effective model for community responsiveness, the visibility of their work in the hospital and community was very low.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".