Maximizing Chronic Disease Prevention and Management through Community Based Participatory Research and Inter-collaborative Practices
Bibliographic record
Abstract
Worldwide societies are aging, giving rise to focus more on chronic disease prevention and management efforts. Such initiatives may be maximized by incorporating a community based participatory research (CBPR), as well as an inter-collaborative practice (ICP) approach. Each strive to garner viable and constructive ideas from varied contributors, so as to help resolve current societal issues and challenges. The aim of this review is to consider use of CBPR and ICP strategies as ways to help ascertain the real and diverse health and social care needs of individuals, groups, and even whole societies. The setting was non-institutional and community based. The method and design utilized a critical social science perspective; a cross-section of the literature was reviewed over a period of three months in 2012. Varied databases were accessed, including PsyLit, Sociofile, PubMed, and Ageline. Conclusions showed that, although scantly addressed; it appears that the utilization of CBPR and ICP strategies may help professionals, community organization personnel, and lay citizens better allocate scarce community resources. Collectively and individually, these versatile strategies may help maximize a given community’s chronic disease management and prevention potential. Towards this on-going end, this paper seeks to contribute.
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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 | 0.138 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| 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".