Shared decision making: what do clinicians need to know and why should they bother?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shared decision making enables a clinician and patient to participate jointly in making a health decision, having discussed the options and their benefits and harms, and having considered the patient's values, preferences and circumstances. It is not a single step to be added into a consultation, but a process that can be used to guide decisions about screening, investigations and treatments. The benefits of shared decision making include enabling evidence and patients' preferences to be incorporated into a consultation; improving patient knowledge, risk perception accuracy and patient-clinician communication; and reducing decisional conflict, feeling uninformed and inappropriate use of tests and treatments. Various approaches can be used to guide clinicians through the process. We elaborate on five simple questions that can be used: What will happen if the patient waits and watches? What are the test or treatment options? What are the benefits and harms of each option? How do the benefits and harms weigh up for the patient? Does the patient have enough information to make a choice? Although shared decision making can occur without tools, various types of decision support tools now exist to facilitate it. Misconceptions about shared decision making are hampering its implementation. We address the barriers, as perceived by clinicians. Despite numerous international initiatives to advance shared decision making, very little has occurred in Australia. Consequently, we are lagging behind many other countries and should act urgently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it