Using diffusion of innovations theory to guide diabetes management program development: an illustrative example
Bibliographic record
Abstract
BACKGROUND: Optimal management of type 2 diabetes requires achievement of optimal glucose, blood pressure and lipid targets through promotion of prudent diet, regular physical activity and adherence to necessary medication. This may require the development of new programs for the coordination of required multidisciplinary services. Diffusion of innovations theory offers a conceptual framework that may facilitate the implementation of such programs. METHODS: To illustrate this, we have re-examined the implementation experiences previously reported by the developers of an actual diabetes management pilot program in Montreal, with an eye toward identifying potentially important process factors that could effectively increase adoption and sustainability. RESULTS: Physician participation in the program appeared to be influenced by perceived advantages of participation, compatibility of the program with own perspective and perceived barriers to participation. Organizational features that may have influenced participation included the extent of the program's integration within the existing health care system. CONCLUSION: A thorough consideration of process factors that impact system and team integration must equally include a focus on ensuring ongoing partnerships among the producers of the model, governments, nongovernmental organizations, private industry, user professionals and patients. This can only be achieved when a knowledge transfer action plan is developed to guide program development, implementation and sustainability.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".