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Record W2043149899 · doi:10.3109/13561820.2014.917613

Student evaluations of an interprofessional education experience in pain management

2014· article· en· W2043149899 on OpenAlexaff
Heather D. Hadjistavropoulos, Karen Juckes, Dale Dirkse, Cathy Cuddington, Kirstie L. Walker, Paul Bruno, G. H. White, Lisa Ruda, Myrna Pitzel Bazylewski

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsRegina Qu'Appelle Health RegionUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsInterprofessional educationPsychosocialContext (archaeology)Pain managementMedical educationHealth professionsHealth carePsychologyHealth professionalsMedicineNursingPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) is vital to healthcare professionals and is especially relevant in the context of pain management. Despite its importance, it is often difficult to provide given limited time and resources and challenges with coordinating schedules across professions. This study explored satisfaction with a one-day IPE workshop on pain management. Seventy-three students from seven professions completed a questionnaire evaluating the workshop. Results suggested that students rated all aspects of the workshop highly, but particularly valued hearing client's experiences with pain. Furthermore, students perceived that their knowledge of pain and interprofessional relationships improved following the workshop. Differences emerged between professions, with students classified as psychosocial reporting greater satisfaction with the IPE than students from biomedical professions. This study supports research previously conducted on IPE in pain management and suggests that when time and resources are constrained, there is value in offering a brief IPE workshop on pain management.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.400
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.517
Teacher spread0.484 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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