Physicians' intentions and use of three patient decision aids
Bibliographic record
Abstract
BACKGROUND: Decision aids are evidence based tools that assist patients in making informed values-based choices and supplement the patient-clinician interaction. While there is evidence to show that decision aids improve key indicators of patients' decision quality, relatively little is known about physicians' acceptance of decision aids or factors that influence their decision to use them. The purpose of this study was to describe physicians' perceptions of three decision aids, their expressed intent to use them, and their subsequent use of them. METHODS: We conducted a cross-sectional survey of random samples of Canadian respirologists, family physicians, and geriatricians. Three decision aids representing a range of health decisions were evaluated. The survey elicited physicians' opinions on the characteristics of the decision aid and their willingness to use it. Physicians who indicated a strong likelihood of using the decision aid were contacted three months later regarding their actual use of the decision aid. RESULTS: Of the 580 eligible physicians, 47% (n = 270) returned completed questionnaires. More than 85% of the respondents felt the decision aid was well developed and that it presented the essential information for decision making in an understandable, balanced, and unbiased manner. A majority of respondents (>80%) also felt that the decision aid would guide patients in a logical way, preparing them to participate in decision making and to reach a decision. Fewer physicians (<60%) felt the decision aid would improve the quality of patient visits or be easily implemented into practice and very few (27%) felt that the decision aid would save time. Physicians' intentions to use the decision aid were related to their comfort with offering it to patients, the decision aid topic, and the perceived ease of implementing it into practice. While 54% of the surveyed physicians indicated they would use the decision aid, less than a third followed through with this intention. CONCLUSION: Despite strong support for the format, content, and quality of patient decision aids, and physicians' stated intentions to adopt them into clinical practice, most did not use them within three months of completing the survey. There is a wide gap between intention and behaviour. Further research is required to study the determinants of this intention-behaviour gap and to develop interventions aimed at barriers to physicians' use of decision aids.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".