Talking the talk in junior interprofessional education: is healthcare terminology a barrier or facilitator?
Bibliographic record
Abstract
BACKGROUND: Use of healthcare terminology is a potential barrier to interprofessional education (IPE). This study describes how junior learners perceive and classify healthcare terminology in IPE settings. METHODS: We conducted a mixed methods study involving 29 medical, 14 nursing, and 2 physician assistant students who had previously attended or were registered to participate in educational activities at McMaster University's Centre for Simulation-Based Learning. 23 participants identified "inclusive" or "exclusive" terminology in a series of scenarios used for IPE workshops using an online survey. We collated lists of "inclusive" and "exclusive" terminology from survey responses, and characterized the frequencies of included words. 22 students participated in focus group discussions on attitudes and perceptions around healthcare terminology after attending IPE workshops. We identified themes through an iterative direct content analysis of verbatim transcripts. RESULTS: Students analyzed 14 cases, identifying on average 21 terms per case as healthcare terminology (28% of overall word count). Of the 290 terms identified, 113 terms were classified as healthcare terminology, 46 as inclusive and 17 as exclusive by > 50% of participants. Analysis of focus group transcripts revealed 4 themes: abbreviations were commonly perceived as complex terminology, lack of familiarity with terminology was often attributed to inexperience, simulation was considered a safe space for learning terminology, and learning terminology was a valued IPE objective. CONCLUSIONS: While students perceive a lot of healthcare terminology in IPE learning materials, categorization of terminology as "inclusive" or "exclusive" is inconsistent. Moreover, healthcare terminology is perceived as a desirable difficulty among junior learners, and should not be avoided in IPE.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".