MétaCan
Menu
Back to cohort
Record W4392471749 · doi:10.1002/lrh2.10412

How to use communities of practice to support change in learning health systems: A landscape of roles and guidance for management

2024· article· en· W4392471749 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLearning Health Systems · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsAlberta Health ServicesAlberta HealthUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta Children's Hospital Research InstituteAlberta InnovatesChildren's Hospital FoundationPublic Health AgencyUniversity of AlbertaAthabasca UniversityPublic Health Agency of CanadaStollery Children’s Hospital FoundationWomen and Children's Health Research InstituteUniversity of LethbridgeChildren's Health Research InstituteKillam TrustsAlberta Health Services
KeywordsKnowledge managementCommunity of practiceCommunity practiceLearning communityCommunity healthFacilitationVariety (cybernetics)Public relationsPsychologyComputer scienceMedicineNursingPolitical sciencePedagogyPublic health

Abstract

fetched live from OpenAlex

Background: Communities of practice support evidence-based practice and can be, in and of themselves, applied learning spaces in organizations. However, the variety of ways that communities of practice can support learning health systems are poorly characterized. Furthermore, health system leaders have little guidance on designing and resourcing communities of practice to effectively serve learning health systems. Methods: We conducted a collective case study, examining a cross-section of Canadian-based communities of practice dedicated to supporting evidence-based practice. We held semi-structured interviews with 21 participants representing 16 communities of practice and 5 community of practice facilitation platforms that provide administration support, tools, and oversight for multiple communities of practice. Using the Conceptual Framework for Value-Creating Learning Health Systems, we characterized the numerous roles that communities of practice can take to support learning health systems. We also pulled insights from the interviews on properly resourcing and managing communities of practice. Results: Communities of practice can advance learning health systems across learning cycles (ie, identifying learning priorities, generating data and knowledge, and implementing and evaluating change). They also act as important infrastructure required to share and coordinate across learning health systems. Community of practice facilitation platforms reduce staff members' workload, in turn, creating greater efficiency and effectiveness across community of practice lifespans. Furthermore, these platforms can be a mechanism to coordinate critical activities (e.g., priority alignment, knowledge brokerage/sharing across the broader system). Conclusion: To the authors' knowledge, this is the first study to characterize communities of practice across the learning health system landscape. With these results, learning health system leaders have a catalog that clarifies the potential communities of practice roles in knowledge generation, implementation, and uptake of new evidence. Furthermore, the results provide evidence that organizational investment in overarching community of practice facilitation platforms will strengthen and accelerate community of practice supports in learning health systems.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.525
GPT teacher head0.621
Teacher spread0.096 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it