Building a virtual community of practice: experience from the Canadian foundation for healthcare improvement’s policy circle
Bibliographic record
Abstract
BACKGROUND: Communities of Practice are formed by people who interact regularly to engage in collective learning in a shared domain of human endeavor. Virtual Communities of Practice (VCoP) are online communities that use the internet to connect people who share a common concern or passion. VCoPs provide a platform to share and enhance knowledge. The Policy Circle is a VCoP that connects mid-career professionals from across Canada who are committed to improving healthcare policy and practice. We wanted to understand the perceived value of the VCoP. METHODS: We used qualitative and quantitative survey research to explore past and current Policy Circle members' thoughts, feelings, and behaviours related to the program. Our research was guided by the Value Creation Framework proposed by Wenger and colleagues. Three surveys were created in collaboration with stakeholders. Data were analyzed within cohort and in aggregate across cohorts. Qualitative data was analyzed thematically, and quantitative data was analyzed using descriptive statistics (means of ranked and scaled responses). RESULTS: Survey participation was high among members (Cohort 1: 67%, Cohort 2: 64%). Participants came from a variety of disciplines including medicine, health policy, allied health, and nursing, with most members having a direct role in health services research or practice. The program was successful in helping participants make connections (mean = 2.43 on a scale from 1 to 5: 1 = yes, significantly, 5 = not at all); variances in both qualitative and quantitative data indicated that levels of enthusiasm within the program varied among individuals. Members appreciated the access to resources; quarterly meetings (n = 11/11), and a curated reading list (n = 8/11) were the most valued resources. Participants reported the development of a sense of belonging (mean = 2.29) and facilitated knowledge exchange (mean = 2.43). At the time of this study, participants felt the program had minor impact on their work (mean = 3.5), however a majority of participants (50%) from Cohort 2 planned to acknowledge the program in their professional or academic endeavours. Through reflective responses, participants expressed a desire for continued and deeper professional network development. CONCLUSIONS: The Policy Circle was successful in facilitating knowledge exchange by creating a community that promoted trust, a sense of belonging and a supportive environment. Members were satisfied with the program; to promote further value, the Policy Circle should implement strategies that will continue member participation and networking after the program is finished.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.057 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".