A SCOPING REVIEW ON BARRIERS AND FACILITATORS OF COMMUNITY ENGAGEMENT IN LEARNING HEALTH SYSTEMS
Bibliographic record
Abstract
Abstract This scoping review identifies the challenges and enablers of community engagement in Learning Health Systems (LHS), an area where elder and health care have growing interests on. LHS aims to improve healthcare delivery, translate knowledge into clinical practice, and emphasize patient-centred outcomes. We followed the Joanna Briggs Institute’s (JBI) scoping review methodology. First, keywords and index terms were identified from MEDLINE and CINAHL databases. Second, using the keywords and index terms identified in the first step, other databases like Web of Science, PsycINFO, and Google were searched. Third, the reference lists of the chosen literature were searched. The results include 25 papers. The Assessing Community Engagement (ACE) Conceptual Model by Aguilar-Gaxiola et al. was utilized to inform the analysis. Active community engagement enhances the relevancy and applicability of health research and fosters trust and rapport between healthcare providers and their communities. It is essential to have appropriate support, mutual respect, culturally sensitive and locally relevant processes with common-ground language. We identified several barriers to engagement: 1) meaningful motivations, 2) logistical challenges in processes, 3) disparities in power dynamics, 4) cultural misunderstanding, and 5) lack of knowledge and skills. The review underscores the need to move beyond tokenistic engagement towards genuine community collaborations. Future research should investigate the facilitators that enhance such meaningful partnerships.
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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 | 0.053 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".