From “Community of Practice” to “Knowledge Building Community”—A qualitative study of project <scp>ECHO</scp> as facilitator of adaptive expertise in frontline community workers
Bibliographic record
Abstract
Abstract Background Health care is fragmented, stigmatizing, and often does not meet the needs of people living with HIV who present to care with significant complexity. Integrated care is an evidence‐based solution, but rarely is enacted across hospital and community settings. Education for community workers that builds capacity toward integrated care is an essential missing piece. Methods Here we describe a qualitative study of the ECHO HIV Psychiatry, a virtual educational series that supports a community of practice of community workers in the HIV sector in Toronto, Canada. The educational series is 9 sessions long and occurs twice/year, reporting here on 4 cycles of the series, from April 2023 to December 2024. Utilizing participant interviews ( n = 29) and ethnographic observation of education sessions, we conducted an abductive analysis, utilizing concepts of adaptive expertise and Knowledge Building Communities (KBCs) to better understand our participant narratives. Adaptive expertise is a theoretical framework in health professions education that describes capabilities that support healthcare workers to navigate complexity in modern healthcare. KBCs in healthcare leverage collaboration and diverse perspectives to support the generation of new solutions. Results Participants' main learning from the ECHO was an approach to caring for clients with significant complexity (including mental health concerns), and the learning mechanisms which supported this include: (1) Explicit value placed on diverse domains of knowledge created psychological safety for risk taking; (2) Perspective exchange with people in different roles facilitated confidence for community workers, as well as epistemic humility (humility about what is known or knowable); and (3) Learning in the ECHO led to new knowledge creation through collaboration and improvisation. Conclusions Results of this study demonstrate how education can support community workers with an approach to complexity, and that this kind of learning may empower community workers to expand the scope of their role, collaborate across hospital and community, and create new solutions to difficult‐to‐solve problems in health care. These are features of a Knowledge Building Community.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".