Extent and Predictors of Decision Regret about Health Care Decisions
Bibliographic record
Abstract
BACKGROUND: People often face difficult decisions about their health and may later regret the choice that they made. However, little is known about the extent of decision regret in health care or its predictors. We systematically reviewed evidence about the extent of decision regret and its risk factors among individuals making health decisions. METHODS: The data sources were Medline, Embase, and reverse citation searches in Google Scholar and Web of Science. Studies using the Decision Regret Scale (DRS) to measure decision regret among individuals making nonhypothetical health decisions were included. There were no restrictions on study design, setting, or language. We extracted characteristics of included studies, measures of central tendency for DRS scores (0 = no regret, 100 = high regret), and all risk factors from published analyses. Quality appraisal was conducted using the Mixed Methods Appraisal Tool. A narrative synthesis was performed owing to the heterogeneity of studies. RESULTS: The initial search yielded 372 unique titles, and 59 studies were included. The overall mean DRS score across studies was 16.5, and the median of the mean scores was 14.3 (standard deviation range = 2.2-34.5) (n = 44 studies). The risk factors most frequently reported to be associated with decision regret in multivariate analyses included higher decisional conflict, lower satisfaction with the decision, adverse physical health outcomes, and greater anxiety levels. CONCLUSIONS: The extent of decision regret as assessed with the DRS in nonhypothetical health decisions was often low but reached high levels for some decisions. Several risk factors related to the decision-making process significantly predicted decision regret. Additional research into the psychometrics of the DRS and the relevance of scores for clinicians and patients would increase the validity of decision regret as a patient-reported outcome.
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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 | 0.040 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".