Decisional Conflict Scale Use over 20 Years: The Anniversary Review
Bibliographic record
Abstract
Background. The Decisional Conflict Scale (DCS) measures 5 dimensions of decision making (feeling: uncertain, uninformed, unclear about values, unsupported; ineffective decision making). We examined the use of the DCS over its initial 20 years (1995 to 2015). Methods. We conducted a scoping review with backward citation search in Google Analytics/Web of Science/PubMed, followed by keyword searches in Cochrane Library, PubMed, Ovid MEDLINE, EMBASE, CINAHL, AMED, PsycINFO, PRO-Quest, and Web of Science. Eligible studies were published between 1995 and March 2015, used an original experimental/observational research design, concerned a health-related decision, and provided DCS data (total/subscales). Author dyads independently screened titles, abstracts, full texts, and extracted data. We performed narrative data synthesis. Results. We included 394 articles. DCS use appeared to increase over time. Three hundred nine studies (76%) used the original DCS, and 29 (7%) used subscales only. Most studies used the DCS to evaluate the impact of decision support interventions ( n = 238, 59%). The DCS was translated into 13 languages. Most decisions were made by people for themselves ( n = 353, 87%), about treatment ( n = 225, 55%), or testing ( n = 91, 23%). The most common decision contexts were oncology ( n = 113, 28%) and primary care ( n = 82, 20%). Conclusions. This is the first study to descriptively synthesize characteristics of DCS data. Use of the DCS as an outcome measure for health decision interventions has increased over its 20-year existence, demonstrating its relevance as a decision-making evaluation measure. Most studies failed to report when decisional conflict was measured during the decision-making process, making scores difficult to interpret. Findings from this study will be used to update the DCS user manual.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".