Bibliographic record
Abstract
There is much focus, appropriately, on the development and delivery of university curricula, so we can 'guarantee' the future of the profession. Reading the academic literature on educational theory and practice, across the health disciplines, reveals active, critical, and robust processes searching for improved educational and professional outcomes. There is development of an evidence base for best-practice teaching and learning. There are excellent resources in Australia, for example, the National Patient Safety Framework, to facilitate integrating basic knowledge of facts into clinical and professional skills development. ewer technological support can contribute to the array and the multiple modes of delivery of education, and may be exciting and more accessible for students. The National Prescribing Curriculum materials, which are online modules provided by the National Prescribing Service for pharmacy, medical and nursing students are excellent examples. n the recent Journal of Clinical Pharmacology and Therapeutics, there is a commentary on teaching therapeutics in US medical schools. In the current US accreditation guidelines for the Association of American Medical Colleges, six core competencies are cited as required for safe and effective prescribing: medical knowledge; patient care; interpersonal skills and communication skills; professionalism; practice-based learning environment; and systems-based practice
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.195 | 0.121 |
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".