Assessment of the DASH-TOP Shared decision-making intervention using the decisional conflict scale: A mixed- method study
Bibliographic record
Abstract
Abstract Background Venous thromboembolism (VTE) in pregnancy is a major cause of maternal morbidity and death. The use of low-molecular-weight heparin (LMWH), despite being the standard of care to prevent VTE, comes with some challenges. Shared decision-making (SDM) interventions are recommended to support patients and clinicians in making preference-sensitive decisions. The quality of the SDM process has been widely assessed with the decisional conflict scale (DCS). Our aim is to report participants’ perspectives of each of the components of an SDM intervention (DASH-TOP) in relation to the different subscales of the DCS. Methods Design: A convergent, parallel, mixed-methods design. Participants: The sample consisted of 22 health care professionals, students of an Applied Clinical Research in Health Sciences (ICACS) master program. Intervention: We randomly divided the participants in three groups: Group 1 received one component (evidence -based information), Group 2 received two components (first component and value elicitation exercises), and Group 3 received all three components (the first two and a decision analysis recommendation) of the SDM intervention. Analysis: For the quantitative strand, we used a non-parametric test to analyze the differences in the DCS subscales between the three groups. For the qualitative strand, we conducted a content analysis using the decisional conflict domains to deductively categorize the responses. Results Groups that received more intervention components experienced less conflict and better decision-making quality, although the differences between groups were not statistically significant. The decision analysis recommendation improved the efficacy with the decision-making process, however there are some challenges when implementing it in clinical practice. The uncertainty subscale showed a high decisional conflict for all three groups; contributing factors included low certainty of the evidence-based information provided and a perceived small effect of the drug to reduce the risk of a VTE event. Conclusions The DASH-TOP intervention reduced decisional conflict in the decision -making process, with decision analysis being the most effective component to improve the quality of the decision. There is a need for more implementation research to improve the delivery of SDM interventions in the clinical encounter.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".