The Clinician Guideline Determinants Questionnaire was developed and validated to support tailored implementation planning
Bibliographic record
Abstract
OBJECTIVES: The purpose of this research was to generate and validate a questionnaire that identifies determinants of guideline use from the clinician perspective. STUDY DESIGN AND SETTING: From January 2017 to March 2018, a seven-member six-country multidisciplinary team used a five-step multimethod design to search for and compile determinant frameworks, map items to determinants (content validity), select the best items for each determinant (content validity), refine wording of determinants and items (face validity), merge or separate items (construct validity), and review the final questionnaire. RESULTS: The Clinician Guideline Determinants Questionnaire includes four sections: clinician demographic information (including two determinants: attitudes about/experience with guidelines), 26 close-ended items reflecting clinician- and guideline-specific determinants, four open-ended items reflecting enablers and barriers perceived as most important, and three items on learning style (preferred sources of guideline information). CONCLUSION: The Clinician Guideline Determinants Questionnaire is a comprehensive, validated instrument that addresses multiple potential determinants specific to guideline use from a clinician perspective. The Questionnaire can be used at multiple time points in the guideline development cycle to assess determinants of the use of new, updated, or adapted guidelines and before and after interventions to assess their impact on the determinants of guideline use. In future research, we will establish psychometric properties of the new questionnaire.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".