The prevention and management of chronic disease in primary care: recommendations from a knowledge translation meeting
Bibliographic record
Abstract
BACKGROUND: Seven chronic disease prevention and management programs were implemented across Quebec with funding support from a provincial-private industry funding initiative. Given the complexity of implementing integrated primary care chronic disease management programs, a knowledge transfer meeting was held to share experiences across programs and synthesize common challenges and success factors for implementation. METHODS: The knowledge translation meeting was held in February 2014 in Montreal, Canada. Seventy-five participants consisting of 15 clinicians, 14 researchers, 31 knowledge users, and 15 representatives from the funding agencies were broken up into groups of 10 or 11 and conducted a strengths, weaknesses, opportunities, and threats analysis on either the implementation or the evaluation of these chronic disease management programs. Results were reported back to the larger group during a plenary and recorded. Audiotapes were transcribed and summarized using pragmatic thematic analysis. RESULTS AND DISCUSSION: Strengths to leverage for the implementation of the seven programs include: (1) synergy between clinical and research teams; (2) stakeholders working together; (3) motivation of clinicians; and (4) the fact that the programs are evidence-based. Weaknesses to address include: (1) insufficient resources; (2) organizational change within the clinical sites; (3) lack of referrals from primary care physicians; and (4) lack of access to programs. Strengths to leverage for the evaluation of these programs include: (1) engagement of stakeholders and (2) sharing of knowledge between clinical sites. Weaknesses to address include: (1) lack of referrals; (2) difficulties with data collection; and (3) difficulties in identifying indicators and control groups. Opportunities for both themes include: (1) fostering new and existing partnerships and stakeholder relations; (2) seizing funding opportunities; (3) knowledge transfer; (4) supporting the transformation of professional roles; (5) expand the use of health information technology; and (6) conduct cost evaluations. Fifteen recommendations related to mobilisation of primary care physicians, support for the transformation of professional roles, and strategies aimed at facilitating the implementation and evaluation of chronic disease management programs were formulated based on the discussions at this knowledge translation event. CONCLUSION: The results from this knowledge translation day will help inform the sustainability of these seven chronic disease management programs in Quebec and the implementation and evaluation of similar programs elsewhere.
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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 | 0.243 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".