Prediction of health professionals' intention to screen for decisional conflict in clinical practice
Bibliographic record
Abstract
OBJECTIVE: To identify the determinants of the intention of physicians to screen for decisional conflict in clinical practice. BACKGROUND: Screening for decisional conflict is one of the key competencies when educating health professionals about shared decision making. Theory-based knowledge about variables predicting their intention to screen for decisional conflict in clinical practice would help design effective implementation interventions in this area. DESIGN: Data of two cross-sectional surveys embedded within a large implementation study of the Ottawa Decision Support Framework (ODSF) in primary care. SETTING AND PARTICIPANTS: In total, 122 health professionals from five family practice teaching units. METHODS: Intention to screen for decisional conflict in clinical practice was defined as the intention to use the clinical version of the Decisional Conflict Scale (DCS) with patients at the end of the clinical encounter. It was assessed at the entry and the exit from this study. Both intentions were entered as a dependent variable in multivariate analyses. MAIN RESULTS: At entry, the intention was influenced by: attitude (P < 0.001), subjective norm (P < 0.001), perceived behavioural control (P < 0.001) and clinical site (P < 0.05). On exit, it was influenced by: subjective norm (P < 0.001), perceived behavioural control (P < 0.001), clinical site (P < 0.05), international Continuing Medical Education (CME) (P < 0.05), other diplomas (P < 0.05) and intervention (P < 0.05). In post hoc analyses, there was a statistically significant difference between entry and exit in the impact of the level of exposure to the multifaceted implementation intervention on the intention (P = 0.003). CONCLUSIONS: Variables predicting the intention of health professionals to screen for decisional conflict in clinical practice using the DCS change over time suggesting that effective implementation interventions in this area will need to be modified longitudinally.
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".