Bibliographic record
Abstract
OBJECTIVE: As shared decision makes increasing headway in healthcare policy, it is under more scrutiny. We sought to identify and dispel the most prevalent myths about shared decision making. METHODS: In 20 years in the shared decision making field one of the author has repeatedly heard mention of the same barriers to scaling up shared decision making across the healthcare spectrum. We conducted a selective literature review relating to shared decision making to further investigate these commonly perceived barriers and to seek evidence supporting their existence or not. RESULTS: Beliefs about barriers to scaling up shared decision making represent a wide range of historical, cultural, financial and scientific concerns. We found little evidence to support twelve of the most common beliefs about barriers to scaling up shared decision making, and indeed found evidence to the contrary. CONCLUSION: Our selective review of the literature suggests that twelve of the most commonly perceived barriers to scaling up shared decision making across the healthcare spectrum should be termed myths as they can be dispelled by evidence. PRACTICE IMPLICATIONS: Our review confirms that the current debate about shared decision making must not deter policy makers and clinicians from pursuing its scaling up across the healthcare continuum.
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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 | 0.122 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.067 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".