143 User experience assessment regarding a shared decision-making web- based tool for thromboprophylaxis during pregnancy
Bibliographic record
Abstract
Introduction Venous thromboembolism (VTE) is a leading cause of maternal morbidity and mortality during pregnancy. Low-molecular-weight heparin (LMWH) is the suggested agent for thromboprophylaxis of VTE in this population. However, clinical guidelines promote shared decision-making (SDM) due to the preference-sensitive nature of this decision. Given the lack of decision aids in this field, we developed an interactive SDM tool (DASH-TOP). Methods We conducted two rounds of semi-structured interviews with ten women at risk of VTE in pregnancy. All interviews were recorded and transcribed. We analyzed data with Nvivo, using a deductive content analysis. Results Details on the findings organized with the honey comb framework domains are provided in figure 1. Overall, DASH-TOP was found to be easy to understand, useful, and desirable. It was also seen as innovative. The second round included a new domain around ‘autonomy of use’, with users reporting that they could navigate the tool independently. Suggestions provided include the possibility of adding personal experiences, explaining more the medical terms, improving visual presentations, and clarifying exercises’ purpose. Discussion Women had a positive experience with DASH-TOP, suggesting appropriate design and usability. Participants provided relevant suggestions including adding personal experiences; clarity on the information provided; and, instructions on the iterative components that will be incorporated. DASH-TOP may be used prior to an encounter in order to facilitate a better dialogue with their clinician. Conclusion Overall, the DASH-TOP tool facilitates informed shared decision-making by providing information often not readily available to patients. Further refinements identified may enhance user experience.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".