MétaCan
Menu
Back to cohort
Record W2938275587 · doi:10.22230/jripe.2019v9n1a275

Interprofessional Learning for Enhanced Patient Safety: Biomedical Engineering Students and Nursing Students in Joint Learning Activities

2019· article· en· W2938275587 on OpenAlexvenueno aff
Janet Mattsson, Britt Östlund, Gunilla Björling, Anna Williamsson, Andréa Eriksson

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupPatient safetyPerspective (graphical)Medical educationHealth careMedicineNursingQualitative researchInterprofessional educationPsychologySociologyComputer science

Abstract

fetched live from OpenAlex

Background: In the last decade, research has highlighted the importance of interprofessional approaches to education and practice. Collaboration between medical practice and engineering has been identified as particularly relevant to developing accountable models for sustainable healthcare and overcoming increased specialization leading to professional barriers. This study aims to analyze insights and understanding expressed by nursing students and biomedical engineering students following a joint learning activity regarding a medical device used in the hospital setting.Method: A qualitative approach deriving from a phenomenological view examined an interprofessional learning activity where the focus was on active integration and knowledge exchange.Conclusion: The activity was expressed as a positive opportunity for getting insights into perspectives from other professional groups as well as insights into the importance of a system perspective in patient safety. The learning and insights listed in the evaluations included ideas about how the two professional groups could collaborate in the future.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.417
Teacher spread0.394 · 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 designOther design
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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research in Interprofessional Practice and EducationSame topicBiomedical and Engineering EducationFrench-language works237,207