International work integrated learning (WIL) in undergraduate paramedicine programs: a cross-sectional survey of practice models in Australasia
Bibliographic record
Abstract
Purpose The proliferation of undergraduate paramedicine programs has led to a surge in demand for work integrated learning (WIL), placing pressure on domestic ambulance service placement capacity. The objective of this study was to establish a baseline understanding of international WIL in paramedicine university programs. Design/methodology/approach A cross-sectional study design was utilized to gather data from Australasian universities offering undergraduate paramedicine. A telephone survey was used to gather quantitative and qualitative data using a tailored questionnaire. Findings Of 15 eligible paramedicine programs, seven program leads participated. All offered international WIL, predominantly short-duration format in locations including United Kingdom, USA, Israel, Nepal, Indonesia, Timor-Leste, New Zealand, South Africa, Finland, Canada and Vanuatu. Two distinct models were identified: academic-accompanied, group “study tours” and unaccompanied individual placements. International WIL is common in paramedicine but placement models, rationale and expected learning experiences are diverse. Originality/value International WIL is an increasing component of paramedicine and other health discipline degrees, yet the pedagogical rationale for their inclusion and typology is not always clear. This paper provides an insight into the variance in international WIL typology in a single health discipline highlighting the heterogeneity and need for future research linking into the structure, support and assessment of international WIL.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".