A Snapshot of Community Engagement in Research in the Context of an Evolving Public Health Paradigm: Review
Bibliographic record
Abstract
BACKGROUND: Community engagement is a work in process. Researchers continue to refine the process of collaboration and look to best practice and lessons learned for guidance in this relatively new model. OBJECTIVE: The aim of this study was to provide a snapshot of whether community engagement has been included in the design and implementation of research initiatives in Australia, Canada and the United Kingdom. The secondary aim is to identify the underlying themes present, to identify theories and tools that drive research. METHODS: A literature search was performed to identify studies that have been conducted to reduce the weight of the general population. RESULTS: The results of the study, which focused on the field of weight loss, indicate that scientific and technological advancements are the primary drivers of research. However, these new research initiatives have largely been undertaken in the absence of community engagement. CONCLUSIONS: The study concludes that initiatives need to adapt to a wider range of stakeholders, develop equitable community engagement platforms and take into consideration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.295 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".