Cultivating a communities of practice for colorectal cancer screening in northern Canada
Bibliographic record
Abstract
BACKGROUND: Knowledge management systems such as a Communities of Practice (CoP) can improve healthcare processes but are challenging in complex multidisciplinary systems, and guidance on methods to establish a CoP are needed. This case illustrates the use of early stakeholder engagement and Nominal Group Technique (NGT) to cultivate a CoP in a complex multidisciplinary system: colorectal cancer screening in northern Canada. METHODS: Stakeholders in the Northwest Territories, Canada were recruited and co-designed a workshop with authors to introduce CoP concepts and identify priorities. At the workshop NGT was used to identify and prioritize gaps in process, practice, and evidence for the CoP to focus on. An anonymous polling system was used to obtain workshop participants' feedback on the process. RESULTS: The co-design process integrated stakeholders' perspectives in developing a workshop. Using NGT, the gap analysis identified 23 areas of focus for the CoP, among which, the highest priorities were identified: communication between clinicians and with patients, and identification of screening eligibility in the electronic medical record. Participants found the process to be useful, educational, and interesting. There was unanimous interest in moving forward with developing a CoP. CONCLUSION: A co-designed workshop and NGT were useful in laying the foundation for a CoP in a complex multidisciplinary environment. POLICY STATEMENT: This case shows the utility of a co-designed workshop and NGT in starting a CoP: a knowledge management system that would provide critical insight into colorectal cancer screening policies for the region.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".