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Record W4385978416 · doi:10.1186/s12909-023-04566-w

Readiness for interprofessional learning among health science students: a cross-sectional Q-methodology and likert-based study

2023· article· en· W4385978416 on OpenAlexafffund
Ana Oliveira, Danielle Brewer‐Deluce, Noori Akhtar‐Danesh, Sarah Wojkowski

Bibliographic record

VenueBMC Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster UniversityWest Park Healthcare Centre
FundersMcMaster University
KeywordsLikert scaleMedical educationHealth scienceInterprofessional educationPsychologyCross-sectional studyScale (ratio)Graduate studentsHealth careMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Interprofessional education (IPE) prepares healthcare students for collaboration in clinical practice, but the effectiveness of this teaching method depends on students' readiness for and perceptions of IPE. Evaluating students' readiness for and perceptions of IPE is challenging, due to the lack of comprehensive measures. This study characterized the level of IPE readiness and perspectives across first-year undergraduate and graduate health science students using the readiness for interprofessional learning Likert Scale (RIPLS) and Q-methodologies. METHODS: This is a cross-sectional, online study. Students were randomized to answer the Likert-scale version of RIPLS (80%) or a matched Q-methodology survey (20%). An ANCOVA compared RIPLS scores between students from different program levels (graduate/undergraduate) and specialization (health professional and general programs). The Q-data was analysed using a by-person factor analysis. RESULTS: Three hundred and four (33% response rate) and 71 (30% response rate) students completed the Likert scale and the Q-methodology surveys, respectively. Students from graduate programs demonstrated high readiness for IPE (higher total RIPLS scores p < 0.001) in comparison to undergraduates. Three factors, associated with program specialization (p = 0.04), emerged from the Q-methodology analysis characterizing students learning priorities. Students in undergraduate general programs were focused on IPE relevance and benefits to "the clinical team", students in graduate programs focused on "the patient", and those in undergraduate health professional programs focused on themselves ("me"). CONCLUSIONS: This novel mixed-methods approach combining traditional Likert-scales with Q-methodology elucidated not only associations between program and specialization with readiness (Likert) but also which components of IPE were valued the most (Q-methodology) and by whom.

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)0.010
metaresearch head score (Gemma)0.015
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.176
GPT teacher head0.617
Teacher spread0.440 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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