Investigating Healthcare Educators' Interprofessional Socialisation Following an Interprofessional Simulation Facilitator Training Programme: A Mixed Methods Study
Bibliographic record
Abstract
AIM: Interprofessional socialisation can contribute to collaborative patient care. Although there is research regarding interprofessional socialisation of healthcare students and frontline staff, there is limited literature regarding healthcare educators in practice settings. Our aim was to examine interprofessional socialisation of healthcare educators in the practice setting following an interprofessional simulation facilitator training programme. DESIGN: Explanatory sequential mixed methods study. METHODS: Quantitative provincial simulation programme evaluation data from 2022 and 2023 (n = 87) were analysed and used to inform qualitative interviews (n = 17). Qualitative and quantitative data were integrated following independent analysis. RESULTS: There was a statistically significant increase in attitudes toward interprofessional socialisation following the simulation facilitator training programme. Qualitative findings revealed themes regarding interprofessional socialisation: (a) benefits gained through interprofessional socialisation, (b) interprofessional, uniprofessional or both, (c) facilitators to interprofessional socialisation, (d) barriers to interprofessional socialisation and (e) opportunities to strengthen interprofessional socialisation. CONCLUSION: Despite positive views of interprofessional socialisation, socialisation behaviours may not be consistent in a variety of contexts. Interprofessional education may increase interprofessional socialisation among educators. IMPLICATIONS FOR THE PROFESSION: It is important to provide interprofessional socialisation opportunities for educators to promote more interprofessional education initiatives. IMPACT: The findings of this study provided insights into how to foster interprofessional socialisation in existing structures and how new pathways might be built to connect educators. REPORTING METHOD: This study is reported in congruence with the Journal Article Reporting Standards-Mixed Methods, Quantitative, and Qualitative Standards provided on the Equator Network. PUBLIC CONTRIBUTION: Members of the provincial simulation team were consulted regarding study design and data collection to optimise participation.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".