Talking the talk in junior interprofessional education: Is healthcare terminology a barrier or facilitator?
Bibliographic record
Abstract
Abstract Background Use of jargon and complex healthcare terminology is a potential barrier to interprofessional education (IPE). Healthcare terminology can be separated into two categories: inclusive terminology shared amongst professions, and exclusive terminology unique to one profession. We sought to understand how complex terminology is perceived by junior learners in an IPE setting. Methods We conducted a mixed methods study involving medical, nursing, and physician assistant students attending IPE simulation workshops. Students reviewed scenarios used in the workshops and identified terminology they considered “inclusive” or “exclusive”. Then, students participated in focus group discussions surrounding attitudes/perceptions towards healthcare terminology. Results 23 students analyzed 14 cases, identifying on average 21 terms per case as healthcare terminology (29% 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, unfamiliarity with terminology was often attributed to being early in training even if exclusive, simulation was considered a safe space for learning, and learning terminology was a valued objective in early IPE. Conclusions Students perceive a lot of healthcare terminology in learning materials, which is recognized as a valuable learning objective in their early IPE experiences, but also a challenge. Categorization of healthcare language is inconsistent among students and may reflect individual differences in prior experiences. Overall, healthcare terminology is a valued 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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| 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".