Validation of a Decision Regret Scale
Bibliographic record
Abstract
BACKGROUND: As patients become more involved in health care decisions, there may be greater opportunity for decision regret. The authors could not find a validated, reliable tool for measuring regret after health care decisions. METHODS: A 5-item scale was administered to 4 patient groups making different health care decisions. Convergent validity was determined by examining the scale's correlation with satisfaction measures, decisional conflict, and health outcome measures. RESULTS: The scale showed good internal consistency (Cronbach's alpha = 0.81 to 0.92). It correlated strongly with decision satisfaction (r = -0.40 to -0.60), decisional conflict (r = 0.31 to 0.52), and overall rated quality of life (r = -0.25 to -0.27). Groups differing on feelings about a decision also differed on rated regret: F(2, 190) = 31.1, P < 0.001. Regret was greater among those who changed their decisions than those who did not, t(175) = 16.11, P < 0.001. CONCLUSIONS: The scale is a useful indicator of health care decision regret at a given point in time.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".