Bibliographic record
Abstract
Discourse regarding the legal position of osteopathic clinical educators is scarce. The educator-patient-student relationship is complex and if not managed well may compromise patient and student safety. This doctrinal research paper explores how Australian Civil Liability Legislation may be applied during clinical education. The paper constitutes a thought experiment and uses reasoning by analogy, applied to a hypothetical problem scenario, set during an osteopathic student's clinical practice event for the purpose of exploring the educator's duty relationships and to tease-out possible acts or omissions that could potentially be used in an argument designed to establish a clinical educators breach of duty. The deliberations presented here illuminate the complex relationships and highlight situations in which a reasonable person may consider the clinical educator has provided poor supervision of the student's work with patients. This concept paper signposts the potential vulnerability of the patient and the student if supervision standards are not maintained and if appropriate clinical standards are not applied. Possible lines of arguments a patient may raise or a student may raise in a negligence case as well as possible defences the clinical educator may offer are presented.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".