Community-based participatory action research: transforming multidisciplinary practice in primary health care
Bibliographic record
Abstract
OBJECTIVES: Health care systems throughout the world are in the process of restructuring and reforming their health service delivery systems, reorienting themselves to a primary health care (PHC) model that uses multidisciplinary practice (MDP) teams to provide a range of coordinated, integrated services. This study explores the challenges of putting the MDP approach into practice in one community in a city in Canada. METHODS: The data we analyzed were derived from a community-based participatory action research (CBPAR) project, conducted in 2004, that was used to enhance collaborative MDP in a PHC center serving a residential and small-business community of 11,000 within a medium-sized city of approximately 300,000 people in Canada. CBPAR is a planned, systematic approach to issues relevant to the community of interest, requires community involvement, has a problem-solving focus, is directed at societal change, and makes a lasting contribution to the community. We drew from one aspect of this complex, multiyear project aimed at transforming the rhetoric advocating PHC reform into actual sustainable practices. The community studied was diverse with respect to age, socioeconomics, and lifestyle. Its interdisciplinary team serves approximately 3,000 patients annually, 30% of whom are 65 years or older. This PHC center's multidisciplinary, integrated approach to care makes it a member of a very distinct minority within the larger primary care system in Canada. RESULTS: Analysis of practice in PHC revealed entrenched and unconscious ideas of the limitations and boundaries of practice. In the rhetoric of PHC, MDP was lauded by many. In practice, however, collaborative, multidisciplinary team approaches to care were difficult to achieve. CONCLUSIONS: The successful implementation of an MDP approach to PHC requires moving away from physician-driven care. This can only be achieved once there is a change in the underlying structures, values, power relations, and roles defined by the health care system and the community at large, where physicians are traditionally ranked above other care providers. The CBPAR methodology allows community members and the health-related professionals who serve them to take ownership of the research and to critically reflect on iterative cycles of evaluation. This provides an opportunity for practitioners to implement relevant changes based on internally generated analyses.
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 | 0.223 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.028 | 0.065 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| 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".