Implementation of the three good questions—A feasibility study in Dutch hospital departments
Bibliographic record
Abstract
OBJECTIVES: To determine the feasibility of pragmatic implementation strategies for three good questions (in Dutch: Drie Goede Vragen; 3GV. What are my options; what are the risks and benefits related to these options; and what does this mean for my situation?) to increase shared decision-making (SDM) efforts in Dutch secondary care, and identify barriers and facilitators of implementation. METHODS: Convergent mixed-method design: pre-post surveys with patients attending one of six clinical departments in a Dutch Hospital, post-intervention interviews with patients and health-care professionals. Primary outcomes: feasibility (reach, use of 3GV). SECONDARY OUTCOMES: SDM, experiences with 3GV and decision making. Interviews focused on barriers and facilitators of 3GV use. Interviews were content coded and categorized into determinants of behaviour change. RESULTS: 35% of the respondents who had heard of 3GV (52%) used all three questions. 3GV use did not lead to more SDM (SDMQ9 M = Δ0.3;SE = 2.2) but patients felt empowered to decide (88%) and to SDM (86%). Barriers were as follows: time investment, other SDM projects and perception that the need to use 3GV differs per patient/consultation. Respondents preferred to use 3GV as they saw fit for the consultation, instead of literally asking them. Facilitators: easy, accessible information materials that can be flexibly used. CONCLUSION: Implementation of 3GV seemed feasible, although influenced by contextual characteristics (eg type of decisions, patients, on-going interventions). 3GV contributed to important elements of SDM, and respondents were willing to apply them in a way that suited their situation. PRACTICE IMPLICATIONS: We recommend continuation of current and new implementation strategies to enable 3GV implementation in secondary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".