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Record W2047006629 · doi:10.1080/13561820600555952

Interprofessional education in palliative care: A pilot project using popular literature

2006· article· en· W2047006629 on OpenAlexaffabout
Pippa Hall, Lynda Weaver, Frances Fothergill‐Bourbonnais, Stephanie Amos, Natalie Whiting, Peter Barnes, Frances Legault

Bibliographic record

VenueJournal of Interprofessional Care · 2006
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsQueen's UniversityOttawa HospitalSaint Paul UniversityUniversity of Ottawa
Fundersnot available
KeywordsCurriculumInterprofessional educationContext (archaeology)Medical educationPalliative careHealth careNursingHealth professionalsDisciplineFocus groupMedicinePsychologyPedagogySociologyPolitical science

Abstract

fetched live from OpenAlex

A need to introduce the concepts of death and dying to the medical and health sciences undergraduate curriculum was identified at the University of Ottawa, Ontario, Canada. As care of the terminally ill is complex and requires the collaborative involvement of a diverse group of health care professionals, an interprofessional educational approach was utilized to address this need. A seminar course was developed using popular literature as the basis for learning, and offered to first and second year medical students, fourth year nursing students and graduate students in spiritual care. The discussion of roles and the provision of care within the context of works of selected literature provided a focus that enabled the students to transcend their disciplinary barriers, and to better understand the perspectives and contributions that other team members bring to patient care. Evaluation findings suggest that meaningful interprofessional education can be introduced effectively to students either prior to or while they are maturing in their professional roles.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0080.003
Scholarly communication0.0020.003
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.033
GPT teacher head0.471
Teacher spread0.438 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations21
Published2006
Admission routes2
Has abstractyes

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