Synergizing expectation and execution for stroke communities of practice innovations
Bibliographic record
Abstract
BACKGROUND: Regional networks have been recognized as an interesting model to support interdisciplinary and inter-organizational interactions that lead to meaningful care improvements. Existing communities of practice within the a regional network, the Montreal Stroke Network (MSN) offers a compelling structure to better manage the exponential growth of knowledge and to support care providers to better manage the complex cases they must deal with in their practices. This research project proposes to examine internal and external factors that influence individual and organisational readiness to adopt national stroke best practices and to assess the impact of an e-collaborative platform in facilitating knowledge translation activities. METHODS: We will develop an e-collaborative platform that will include various social networking and collaborative tools. We propose to create online brainstorming sessions ('jams') around each best practice recommendation. Jam postings will be analysed to identify emergent themes. Syntheses of these analyses will be provided to members to help them identify priority areas for practice change. Discussions will be moderated by clinical leaders, whose role will be to accelerate crystallizing of ideas around 'how to' implement selected best practices. All clinicians (~200) involved in stroke care among the MSN will be asked to participate. Activities during face-to-face meetings and on the e-collaborative platform will be documented. Content analysis of all activities will be performed using an observation grid that will use as outcome indicators key elements of communities of practice and of the knowledge creation cycle developed by Nonaka. Semi-structured interviews will be conducted among users of the e-collaborative platform to collect information on variables of the knowledge-to-action framework. All participants will be asked to complete three questionnaires: the typology questionnaire, which classifies individuals into one of four mutually exclusive categories of information seeking; the e-health state of readiness, which covers ten domains of the readiness to change; and a community of practice evaluation survey. SUMMARY: This project is expected to enhance our understanding of collaborative work across disciplines and organisations in accelerating implementation of best practices along the continuum of care, and how e-technologies influence access, sharing, creation, and application of knowledge.
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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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".