Building community partnerships for diabetes primary prevention: lessons learned
Bibliographic record
Abstract
Purpose To describe the process of building partnerships between a health professional group (university‐based researchers and organizations from within and outside the health sector) and the black communities, highlight the accomplishments and identify problems in the process. Design/methodology/approach The description of the process of building partnerships with four black communities in Nova Scotia is organized in the following sections: the impetus for launching a Diabetes Primary Prevention for the Black Communities Project, its preparation, implementation, and evaluation. The accomplishments and the problems associated with the Project are analysed. Findings Recruitment of participants for the focus groups was challenging. Response rate to survey questionnaire was moderate. Presentation of the Project results by one of the black Project assistants to the participant communities was well received. The Project was quite successful in encouraging community involvement by engaging community groups in several small‐scale activities. Three issues related to project implementation were identified: recruitment of focus groups, participant disappointment, and survey return rates. Strategies incorporating the principles of involving a target audience, providing a service, empowering people and respecting cultural diversity with the aim to ensure successful partnership building with the black communities were proposed. Originality/value This paper describes the process of forging partnership with the black communities. The results of the Project could serve as a paradigm for developing culturally sensitive and responsive strategies to lessen the burden of type 2 diabetes in other racial minority communities.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".