Doctors’ views of clinical practice guidelines: a qualitative exploration using innovation theory
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The authors undertook this qualitative study as part of a larger evaluation of the effect of eight clinical practice guidelines issued by an arm's-length government agency in a Canadian province. Using Orlandi and colleagues' version of the Rogers diffusion of innovation model as a framework, the authors mapped doctors' views on implementation of clinical practice guidelines. METHODS: In semi-structured interviews with 45 representative doctors, the authors elicited doctors' framework of meaning for behaviour change in general and for clinical practice guideline uptake in particular. These were then compared with the adapted Orlandi/Rogers diffusion of innovation model to confirm, amend or challenge that model. RESULTS: Doctors identified the following influences on changes to their clinical practice and on clinical practice guideline uptake, within a five-step innovation model: 1 innovation: evidence change is required, perceived need for change; 2 communication: awareness of innovation; 3 adoption: evidence of improved outcomes without increased patient risk, opinion leader support, consistency with current trends; 4 implementation: patient and family acceptability; and 5 maintenance: system support, patient and family support, observed improved patient outcomes without increased risk. CONCLUSIONS: Innovation for doctors is a complex decision process rather than a single decision point. Change occurs in the context of professional networks and patient and family support and demand.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".