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Record W2008763409 · doi:10.3109/13561820.2014.998364

The evaluation of a national interprofessional palliative care workshop

2015· article· en· W2008763409 on OpenAlexaffabout
Sharon Kaasalainen, Kathleen Willison, Abigail Wickson‐Griffiths, Alan Taniguchi

Bibliographic record

VenueJournal of Interprofessional Care · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInterprofessional educationPalliative careVariety (cybernetics)Medical educationPerceptionHealth professionalsNursingPsychologyIdentity (music)Health careQualitative researchMedicineSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the impact of a palliative/end-of-life care workshop on students' perceptions of professional identity, team understanding, and their readiness for interprofessional education (IPE). A before-and-after design was used combining both qualitative and quantitative methods. A survey was completed by 25 undergraduate students from a variety of health care professional schools across Canada, both before and after they attended the five-day workshop. There was a significant increase in students' readiness for IPE, perceptions of professional identity, and team understanding after they attended the palliative care workshop. Students stated that learning about other professionals' backgrounds and becoming more sensitive to other team members and their scopes of practice helped change the way they would practice. The findings from this study will contribute to our understanding of student attitudes around IPE and palliative care.

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)0.016
metaresearch head score (Gemma)0.034
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 score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.148
GPT teacher head0.548
Teacher spread0.400 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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