Knowledge translation of clinical practice guidelines among neurologists: A mixed-methods study
Bibliographic record
Abstract
OBJECTIVES: Clinical practice guidelines have the potential to improve care, but are often not optimally implemented. Improving guideline use in clinical practice may improve care. The objective of this study was to identify the barriers and facilitators (determinants) of guidelines use among neurologists and to propose a strategy to improve guideline implementation. METHODS: This was a mixed-methods study design. A quantitative, population-based, cross-sectional survey of Canadian neurologists was conducted. Associations between guidelines use and determinants of guidelines use were examined. Focus groups and interviews were conducted using purposeful sampling of the population. Determinants of guideline use were mapped to interventions to establish a strategy for guideline implementation among neurologists. RESULTS: 38.7% (n = 311) of neurologists responded to the survey. Typically, respondents had been practicing for 16.6 years and worked in an academic institution in an urban setting. Being male and having an academic affiliation was associated with guideline use. Determinants of guideline use differed between guideline users and non-users; non-users consistently rating determinants lower than users, especially applicability. Two focus groups and one interview (n = 11) identified six main themes of determinants of guideline use: Credibility, knowledge, applicability, resources, motivation, and target audience; which was congruent with the quantitative data. The proposed knowledge translation strategy contains three pillars: guidelines development, dissemination, and interventions. CONCLUSIONS: Several determinants of guideline use not commonly discussed in the literature were identified (applicability, target audience, credibility). The proposed implementation strategy is a valuable resource for guideline developers and policy/decision-makers to improve knowledge translation of guidelines among neurologists.
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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 | 0.071 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".