Clinicians' assessments of practice guidelines in oncology: The CAPGO survey
Bibliographic record
Abstract
OBJECTIVES: Promoting the adoption of clinical practice guidelines is a challenging task. Research shows that adopters' beliefs about guidelines and the process of their development are important predictors of guideline use. Our objective is to report on the development and refinement of an instrument designed to measure oncologists' assessments of guidelines in oncology. METHOD: A theoretically derived instrument was drafted. Oncologists were asked to complete a questionnaire for each draft guideline they were asked to review. Factor analyses and multilevel modeling techniques were undertaken to explore the properties of the instrument and refine it. RESULTS: A total of 488 Ontario clinicians were sent 1494 new questionnaires regarding 34 clinical practice guidelines produced between 1999 and 2002. A refined eighteen-item questionnaire with four stable factors that predicted 60 percent of the variance emerged. The factors are interpreted as guideline quality, applicability, acceptability, and comparative value. The four factors predicted oncologists' endorsements of draft guidelines and, with the exception of quality, predicted their intentions to use the guidelines. As expected, variation in the factor scores could be attributed more to the differences among the oncologist who completed the survey than to the differences among the guidelines themselves. CONCLUSIONS: An instrument composed of four stable and theoretically relevant factors emerged. The findings support the hypothesis that beliefs about guideline attributes and development attributes relate to oncologists' endorsement of and intentions to use guidelines. Our next step is to link the responses of the survey with actual clinical behavior.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".