Needs assessment for the creation of a community of practice in a community health navigator cohort
Bibliographic record
Abstract
BACKGROUND: Community Health Navigators (CHNs) are members of a patient's care team that aim to reduce barriers in accessing healthcare. CHNs have been described in various healthcare settings, including chronic disease management. The ENhancing COMmunity health through Patient navigation, Advocacy, and Social Support (ENCOMPASS) program of research employs CHNs, who are trained to improve access to care and community resources for patients with multiple chronic diseases. With complex and demanding roles, it is essential that CHNs communicate with each other to maintain knowledge exchange and best practices. A Community of Practice (CoP) is a model of situated learning that promotes communication, dedication, and collaboration that can facilitate this communication. The objective of this study was to engage with CHNs to determine how a CoP could be implemented to promote consistency in practices and knowledge for CHNs across primary care sites. METHODS: A needs assessment for a CHN CoP was conducted using sequential steps of inquiry. A preliminary focused literature review (FLR) was done to examine the ways in which other healthcare CoPs have been implemented. Results from the FLR guided the creation of an exploratory survey and group interview with key informants to understand best approaches for CoP creation. Political, economic, social, and technological (PEST) and strengths, weaknesses, opportunities, and threats (SWOT) analyses synthesized results in a comprehensive manner for strategic recommendations. RESULTS: The FLR identified different approaches and components of healthcare CoPs and guided analyses of mitigatable risk factors and leverageable assets for the intervention. The survey and group interview revealed an informal and effective CoP amongst current CHNs, with preferred methods including coffee meetings, group trainings, and seminars. A well-maintained web platform with features such as an encrypted discussion forum, community resource listing, calendar of events, and semi-annual CHN conferences were suggested methods for creating an inter-regional, formal CoP. CONCLUSION: The study findings recognise the presence of an informal CoP within the studied CHN cohort. Implementation of a formal CoP should complement current CoP approaches and aid in facilitating expansion to other primary care centres utilizing digital communication methods, such as a comprehensive web platform and online forum.
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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 | 0.038 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".