Reaching the Underserved Through Community-Based Participatory Research and Service Learning
Bibliographic record
Abstract
OBJECTIVES: To provide an overview of the Community Health Fellowship Program (CHFP), describe the types of projects completed by the community health fellows from 2005 to 2009 and to assess the program's effectiveness from the perspective of fellows and community partners. METHODS: We developed the CHFP for training medical students in community-based participatory research (CBPR), and understanding the components of successful community partnerships for addressing health disparities in underserved communities. The program has didactic and applied community research components. RESULTS: From 2005 to 2009, fellows completed 25 research projects with 19 different community partners. Fellows reported favorable attitudes about the program, their mentors, and their community projects; their research knowledge increased significantly in most areas, especially their ability to develop a succinct research question, familiarity with CBPR, and delivering a formal research presentation (Wilcoxon signed-rank test, P <.05). Community partners reported favorable attitudes toward the fellows and the program; using a 5-point Likert scale (1 = not favorable, 5 = very favorable), they reported highly favorable attitudes about fellows' level of responsibility (4.85), level of cooperation (4.85), familiarity with the needs of the medically underserved (4.69), and knowledge of how to apply local solutions to health problems (4.54). CONCLUSIONS: The CHFP has high favorability and support among fellows and community partners; the program can serve as a prototype for training future physicians in understanding and addressing the needs of the underserved, through community partnerships, and community-based participatory research.
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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 |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.068 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".