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Record W3089484050 · doi:10.1016/j.ijosm.2020.10.003

Duty of care in clinical education - Part 1

2020· article· en· W3089484050 on OpenAlexfundno aff
Keri Moore

Bibliographic record

VenueInternational journal of osteopathic medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
FundersOvarian Cancer Canada
KeywordsDutyMedicineArgument (complex analysis)AnalogyCompromiseLiabilityDuty of careSet (abstract data type)LegislationVulnerability (computing)Clinical PracticeEngineering ethicsMedical educationLawNursingEpistemologyPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.129
GPT teacher head0.543
Teacher spread0.413 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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