Bibliographic record
Abstract
In healthcare, 'shared decision making' (SDM) refers to when both the patient and clinician collaborate to determine the best course of care or treatment. Despite its growing importance, SDM isn't consistently practiced. Doctors play a key role in its application, leading this thesis to explore how to effectively teach SDM to young doctors and examine the factors influencing its adoption in practice, such as doctor culture, personal beliefs, cognitive processes, and contextual factors. The research reveals that most SDM training programs in healthcare fall short in providing effective learning experiences, lacking experiential learning and opportunities for reflective practice. Even when doctors receive patient feedback, it does not always translate into improved decision-making, often due to insufficient mentor guidance. Additionally, doctors' culture, emphasizing medical evidence and autonomy in decision-making, affects their approach to SDM. Challenges arise when care becomes complex, leading to inconsistencies between traditional practices and collaborative decision-making. Despite efforts to empower patients, doctors often still make decisions on their behalf. Effective SDM requires active doctor involvement to tailor care to each patient's unique needs, yet doctors may struggle in this regard. Initiatives to enhance SDM must recognize its complexity and leverage insights from other disciplines to support professional learning and change. The study employed various research methods, including a systematic review of SDM training initiatives, interviews with healthcare providers, patient questionnaires, and an anthropological study exploring doctor culture's impact on SDM. Research findings are qualitatively analyzed, drawing on diverse social science theories.
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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 | 0.067 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".