Mobilizing community-driven health promotion through community granting programs: a rapid systematic review
Bibliographic record
Abstract
BACKGROUND: Effective health promotion responds to the unique needs of communities. Community granting programs that fund community-driven health promotion initiatives are a potential mechanism to meet those unique needs. While numerous community health-focused programs are available, the various strategies used by granting programs to foster engagement, administer grants and support awardees have not been systematically evaluated. This rapid systematic review explores the administration of community granting programs and how various program components impact process and population health outcomes. METHODS: A systematic search was conducted across three databases: Medline, SocINDEX, and Political Science Database. Single reviewers completed screening, consistent with a rapid review protocol. Studies describing or evaluating community granting programs for health or public health initiatives were included. Data regarding program characteristics were extracted and studies were evaluated for quality. A convergent integrated approach was used to analyze quantitative and qualitative findings. RESULTS: Thirty-five community granting programs, described in 36 studies, were included. Most were descriptive reports or qualitative studies conducted in the USA. Program support for grant awardees included technical assistance, workshops and training, program websites, and networking facilitation. While most programs reported on process outcomes, few reported on community or health outcomes; such outcomes were positive when reported. Programs reported that many funded projects were likely sustainable beyond program funding, due to the development of awardee skills, new partnerships, and securing additional funding. From the perspectives of program staff and awardees, facilitators included the technical assistance and workshops provided by the programs, networking amongst awardees, and the involvement of community members. Barriers included short timelines to develop proposals and allocate funds. CONCLUSIONS: This review provides a comprehensive overview of health-related community granting programs. Grant awardees benefit from technical assistance, workshops, and networking with other awardees. Project sustainability is enhanced by the development of new community partnerships and grant-writing training for awardees. Community granting programs can be a valuable strategy to drive community health, with several key elements that enhance community mobilization. REGISTRATION: PROSPERO #CRD42023399364.
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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 | 0.076 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.015 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.024 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".