Readiness for interprofessional learning among health science students: a cross-sectional Q-methodology and likert-based study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".