Building Capacity for Community-Based Participatory Research for Health Disparities in Canada: The Case of “Partnerships in Community Health Research”
Bibliographic record
Abstract
Enthusiasm for community-based participatory research (CBPR) is increasing among health researchers and practitioners in addressing health disparities. Although there are many benefits of CBPR, such as its ability to democratize knowledge and link research to community action and social change, there are also perils that researchers can encounter that can threaten the integrity of the research and undermine relationships. Despite the increasing demand for CBPR-qualified individuals, few programs exist that are capable of facilitating in-depth and experiential training for both students and those working in communities. This article reviews the Partnerships in Community Health Research (PCHR), a training program at the University of British Columbia that between 2001 and 2009 has equipped graduate student and community-based learners with knowledge, skills, and experience to engage together more effectively using CBPR. With case studies of PCHR learner projects, this article illustrates some of the important successes and lessons learned in preparing CBPR-qualified researchers and community-based professionals in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.173 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.059 | 0.063 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".