Implementation of shared decision-making in healthcare policy and practice: a complex adaptive systems perspective
Bibliographic record
Abstract
Background: Despite the suggested benefits of shared decision-making (SDM), its implementation in policy and practice has been slow and inconsistent. Use of complex adaptive systems (CAS) theory may provide understanding of how healthcare system factors influence implementation of SDM. Methods: Using the example of choice of mode of birth after a previous caesarean section, in-depth, semi-structured interviews were conducted with patients, providers, and decision makers in British Columbia, Canada, to explore the system characteristics and processes that influence implementation of SDM. Implementation and knowledge translation principles guided study design, and constructionist grounded theory informed iterative data collection and analysis. Findings: Analysis of interviews (n=58) revealed that patients formed early preferences for mode of delivery (after the primary caesarean) through careful deliberation of social risks and benefits. Physicians acted as information providers of clinical risks and benefits, while decision makers revealed concerns related to liability and patient safety. These concerns stemmed from perceptions of limited access to surgical resources, which had resulted from budget constraints. Discussion and conclusions: To facilitate the effective implementation of SDM in policy and practice it may be critical to initiate SDM once patients become aware of their healthcare options, assist patients to address the social risks that influence their preferences, manage perceptions of risk related to patient safety and litigation among physicians, and enhance access to healthcare resources.
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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 | 0.066 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".