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Evaluation of the perceived impact of an interdisciplinary healthcare ethics course on clinical practice

2004· article· en· W2148728459 on OpenAlexafffund
Christine Carpenter, Janet Ericksen, Barbara Purves, David S. Hill

Bibliographic record

VenueLearning in Health and Social Care · 2004
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsHealth careCurriculumMedical educationPerceptionHealth professionalsDiversity (politics)Focus groupInterprofessional educationPsychologyEngineering ethicsCourse evaluationNursingMedicineHigher educationPedagogySociologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Healthcare professionals and educators perceive that interdisciplinary education initiatives facilitate improved interdisciplinary team functioning and practice in a diversity of healthcare settings, but evidence to support this perception has been lacking. This report presents data from a descriptive study conducted in two phases – a focus group and the development and administration of a questionnaire – that sought to evaluate healthcare professionals’ perception of the impact of having taken an interdisciplinary course in healthcare ethics on the subsequent clinical practice of healthcare professionals, and to identify implications for future ethics‐related, interdisciplinary course development. The course organization and the course participants and faculty are briefly described. The findings indicate that respondents judged the course to be valuable in enhancing their ability to engage in the interdisciplinary aspects of practice and in addressing ethical issues. They identified effective components of the education experience and areas in which the course organization could be improved. Based on these findings, implications for future curriculum and course planning and evaluation research are discussed.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
GPT teacher head0.662
Teacher spread0.468 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations14
Published2004
Admission routes2
Has abstractyes

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