Dalhousie University's communities of practice: Part 2. Research collaborations.
Bibliographic record
Abstract
�he commitment of Dalhousie University’s faculty of dentistry to improving access to care is visible in the innovative and supportive partnerships among dentists, dental hygienists, academics, care providers, policymakers and community members the faculty has established over the years. These partner ships are effective communities of practice that bring together a group of people who share a common concern or interest and who learn through interacting with each other. 1 Communities of practice can take many forms, including voluntary informal networks, worksupported formal education sessions, apprentice training or multidisciplinary, multi-site project teams. 2 Communities of practice can be a means of bridging the gap between researchers and the practitioners and policy-makers who make use of research outcomes, 3 with knowledge transfer flowing in both directions. The process of know ledge transfer entails a complex series of actions that “encompasses all steps between the creation of new knowledge and its application to yield beneficial outcomes for society.” 4 Coalitions such as communities of practice in dental education and research may lead to new models of oral care that will help to alleviate the increasing demands placed upon our current systems of oral care. 5 The Collaboration of Oral Health Researchers (COHR), a research group founded by Drs. Mark Filiaggi and Debora Matthews, has been a productive and dynamic community of practice since its early beginnings in 2004. The COHR mission is to improve the oral health of underserved populations and to build capacity for oral health-related research. Through the develop ment of strong collaborative partnerships with decision-makers, stakeholders and the community, we are building necessary links between population needs assessment and delivery of oral health services. Concurrent with the creation of the COHR, we also began a significant and sustained part nership with the Atlantic Health Promotion Research Centre to create the Oral Health of Seniors research group. Over time, our coalitions have shared research knowledge and resources to form well-rounded and diverse groups with a common interest in increasing access to oral health care for vulnerable populations. Over the past 10 years, COHR researchers have been awarded over one million dollars of research and knowledge translation funding focusing on the oral health of older adults.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.014 |
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".