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Record W2539391276 · doi:10.1186/s12909-016-0797-8

Measuring interprofessional competencies and attitudes among health professional students creating family planning virtual patient cases

2016· article· en· W2539391276 on OpenAlexafffundabout
Eric C. Wong, Jasmine Leslie, Judith A. Soon, Wendy V. Norman

Bibliographic record

VenueBMC Medical Education · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsProvincial Health Services AuthorityB.C. Women's Hospital & Health CentreWomen's Health Research InstituteUniversity of British Columbia
FundersInstitute of Health Services and Policy ResearchCanadian Institutes of Health ResearchProvincial Health Services AuthorityMichael Smith Health Research BC
KeywordsInterprofessional educationMedical educationLikert scaleHealth carePharmacyTest (biology)PsychologyMedicineWilcoxon signed-rank testVirtual patientHealth scienceReproductive healthCurriculumNursingPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: The Virtual Interprofessional Patients-Computer-Assisted Reproductive Health Education for Students (VIP-CARES) Project took place during the summers of 2010-2012 for eight weeks each year at the University of British Columbia (UBC). Undergraduate health care students worked collaboratively to develop virtual patient case-based learning modules on the topic of family planning. The purpose of this study was to evaluate the changes in perception towards interprofessional collaboration (IPC) among the participants, before and after the project. METHODS: This study utilized a mixed methods evaluation using self-assessment survey instruments, semi-structured interviews, and reflective essays. Pre- and post- project surveys were adapted from the Canadian Medical Education Determinants (CanMEDS) and Canadian Interprofessional Health Collaborative (CIHC) frameworks, as well as the Memorial University Interprofessional Attitudes (IPA) questionnaire. The survey results were analyzed as mean (M) and standard deviation (SD) on Likert scales. The non-parametric Wilcoxon signed-rank test was used to determine if any significant changes were measured between each participant's differences in score (p ≤ 0.05). Post-project interview transcripts and essays were analyzed using recursive abstraction to elicit any themes. RESULTS: Altogether, 26 students in medicine, pharmacy, nursing, midwifery, dentistry, counselling psychology, and computer science participated in VIP-CARES, during the three years. Student attitudes toward IPC were positive before and after the project. At the project's conclusion, there was a statistically significant increase in the participants' self-assessment competency scores in the CanMEDS roles of health advocate (p = 0.05), manager (p = 0.02), and medical expert (p = 0.03), as well as the CIHC domains of interprofessional communication (p = 0.04), role clarification (p = 0.01), team functioning (p = 0.05), and collaborative leadership (p = 0.01). Qualitative evaluations yielded three major themes: communication and respect as key to team functioning, importance of role clarification within the team, and existence of inherent challenges to IPC. From the reflections, students generally felt more comfortable with their improvements in the CIHC domains of interprofessional communication, team functioning, and role clarification. CONCLUSION: After working within an interdisciplinary team developing virtual patient learning modules on family planning, the student participants of the VIP-CARES Project indicated general improvement in the skills necessary for effective interprofessional collaboration. Triangulation of the overall data suggests this was especially observed within the areas of interprofessional communication, team functioning, and role clarification.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.083
GPT teacher head0.467
Teacher spread0.383 · 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

Citations31
Published2016
Admission routes3
Has abstractyes

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