From words to action: time for Australia to take shared decision making implementation seriously
Bibliographic record
Abstract
Why is embedding shared decision making within the Australian health care system essential and urgent? Shared decision making is a process of engagement and partnership between a patient and their clinician that enables a collaborative decision to be made based on the best evidence, individual circumstances, and what matters most to the patient.1 Patient involvement in making informed health decisions is a fundamental right2 and is central to safe and quality health care. Shared decision making represents the highest standard of informed consent3 and is a cornerstone of value-based health care. As well as benefitting individual patients and clinicians, shared decision making also has an important role in addressing unwarranted variations in health care and has the potential to contribute to health system sustainability by reducing the overuse of low-value care (where the benefits do not, or hardly, outweigh the harms) and increasing the uptake of care that is known to be effective but is underutilised.
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.
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 | 0.131 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.029 | 0.045 |
| Open science | 0.008 | 0.048 |
| Research integrity | 0.025 | 0.054 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".