A blended learning for general practitioners and nurses on skills to support shared decision-making with patients about palliative cancer treatment: A one-group pre-posttest study
Bibliographic record
Abstract
OBJECTIVE: To evaluate a newly developed blended learning programme for general practitioners (GPs) and nurses in supporting shared decision making (SDM) about palliative cancer treatment in a simulated setting. METHODS: In a pre-posttest study, healthcare professionals (HCPs) participated in the blended learning (i.e. e-learning and (online) training session). HCPs filled out surveys (T0 (baseline), T1 (after e-learning) and T2 (after full blended learning)) and engaged in simulated consultations at T0 and T2. The primary outcome was observed SDM support (Triple-S; DSAT-10 for validation). Secondary outcomes included satisfaction, knowledge about and attitude towards SDM support. Repeated measures General Linear Models were conducted. RESULTS: 33 HCPs (17 GPs and 16 nurses) participated. SDM support significantly improved after training (Triple-S; medium effect). Observers' overall rating of SDM support (medium effect) as well as HCPs' knowledge (large effect) and beliefs about their capabilities (medium effect) improved after training. There was no difference in decision support skills (DSAT-10), HCPs' other clinical behavioural intentions and satisfaction. HCPs evaluated the training positively. CONCLUSION: Blended learning for HCPs on supporting SDM in palliative cancer care improved their skills, knowledge and confidence in simulated consultations. PRACTICE IMPLICATIONS: These first findings are promising for evaluating interprofessional SDM in clinical practice.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".