Professional Virtual Communities for Health Care Implementers: Impact of Participation on Practice
Bibliographic record
Abstract
Background: Since 2008, GHDonline.org has provided a platform of professional virtual communities (PVCs) for health care implementers around the world to connect and discuss delivery challenges. Initially focused on low-resource settings internationally, GHDonline received funding from the Agency for Healthcare Research and Quality (AHRQ) in 2013 to expand the platform and launch the US Communities Initiative (USCI), PVCs for US-based health care professionals working with underserved populations. Objective: Over the course of the three-year funding period, we established four PVCs focused on population health, quality and safety, costs of care, and delivery innovations. We aim to develop a greater understanding of the challenges facing US health care professionals while also facilitating the dissemination and translation of evidence-based resources and novel approaches to delivering care. We seek to understand the impact that participation in these PVCs has on the implementation and integration of best practices in care delivery around the country. Methods: Each PVC is supported by a team of expert moderators who guide and shape community goals, content, and programming. GHDonline works closely with these moderators to organize virtual Expert Panels (week-long, asynchronous online conferences), which facilitate the spread of evidence-based resources and, through dialogue with experts, educate members on strategies for adapting these tools for a range of delivery settings. Our impact evaluation includes three methods: analysis of site data, member surveys, and phone interviews. Site data shows the scope and engagement of readership in the PVCs. Surveys, fielded before and after each Expert Panel, assess members’ knowledge of and ability to implement relevant best practices. Individual interviews identify examples of PVC participation impacting practice, as well as opportunities to improve the PVCs themselves. Results: While evaluation efforts are ongoing, current survey data shows a majority of respondents, 91% (149/163), found information shared in Expert Panels relevant to the populations they serve. A strong majority, 73% (127/175), report an intention to make changes in their practice, and 47% (81/172) report implementing changes based on knowledge gained through PVC participation. We randomly selected 500 active members to participate in interviews and have completed 50 interviews to date. A significant majority of interviewees, 82% (41/50), recommended GHDonline to colleagues, and many, 60% (30/50), indicated they are making changes in their practice based on information gained through PVC participation. Conclusions: Recognizing the limitations of self-reported surveys and interview responses, and the preliminary nature of our current findings, we believe these results show strong potential for PVCs to facilitate dissemination and translation of evidence-based practices and improve care delivery in the US. Trial Registration: Not applicable.
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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 | 0.093 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".