Are you SURE
Bibliographic record
Abstract
OBJECTIVE To assess the reliability and validity of the 4-item SURE (Sure of myself; Understand information; Risk-benefit ratio; Encouragement) screening test for decisional conflict in patients. DESIGN Cross-sectional study. SETTING Four family medicine groups in Quebec and 1 rural academic medical centre in New Hampshire. PARTICIPANTS One hundred twenty-three French-speaking pregnant women considering prenatal screening for Down syndrome and 1474 English-speaking patients referred to watch condition-specific video decision aids. MAIN OUTCOME MEASURES Cronbach α was used to assess the reliability of SURE. A factorial analysis was performed to assess its unidimensionality. The Pearson correlation coefficient was computed between SURE and the Decisional Conflict Scale to assess concurrent validation. A t test procedure comparing the SURE scores of patients who had made decisions with the scores of those who had not was used to assess construct validation. RESULTS Among the 123 French-speaking pregnant women, 105 (85%) scored 4 out of 4 (no decisional conflict); 10 (8%) scored 3 (≤ 3 indicates decisional conflict); 7 (6%) scored 2; and 1 (1%) scored 1. Among the 1474 English-speaking treatment-option patients, 981 (67%) scored 4 out of 4; 272 (18%) scored 3; 147 (10%) scored 2; 54 (4%) scored 1; and 20 (1%) scored 0. The reliability of SURE was moderate (Cronbach α of 0.54 in French-speaking pregnant women and 0.65 in treatment-option patients). In the group of pregnant women, 2 factors accounted for 72% of the variance. In the treatment-option group, 1 factor accounted for 49% of the variance. In the group of pregnant women, SURE correlated negatively with the Decisional Conflict Scale ( r = −0.46; P < .0001); and in the group of treatment-option patients, it discriminated between those who had made a choice for a treatment and those who had not ( P < .0001). CONCLUSION The SURE screening test shows promise for screening for decisional conflict in both French- and English-speaking patients; however, future studies should assess its performance in a broader group of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".