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Record W2785830992 · doi:10.5430/jnep.v8n7p38

A student-led interprofessional workshop on conflict management style

2018· article· en· W2785830992 on OpenAlexaffvenueabout
Charlotte Lee, Katrina Arellano, Lauren Lovold, Vanessa Mesaglio, Sanne Kaas-Mason

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsOttawa Public HealthToronto Western HospitalUniversity Health NetworkToronto Metropolitan University
Fundersnot available
KeywordsInterprofessional educationMedical educationNursingPsychologyConflict managementStyle (visual arts)Service (business)MedicineHealth careSociologyPolitical science

Abstract

fetched live from OpenAlex

Student leadership in interprofessional education is known to bring positive influences to learning collaborative skills for nursing and other health professional trainees. Yet a scarce number of student-led interprofessional activities are described in the literature. This report describes one student-led interprofessional education workshop that is facilitated by personnel from a faculty interprofessional education program. Undergraduate students (N = 23) from nursing, allied health and social service training programs at one university in Toronto, Canada, participated in a one-time workshop regarding conflict management style. Findings from our evaluation survey showed that the workshop was well-received by participants and demonstrated utility and feasibility. Such outcomes provide supportive evidence for fostering student leadership in designing and implementing interprofessional teaching.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.098
GPT teacher head0.571
Teacher spread0.473 · 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 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

Citations1
Published2018
Admission routes3
Has abstractyes

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