Examining Professional Stereotypes in an Interprofessional Education Simulation Experience
Bibliographic record
Abstract
Background: Interprofessional education (IPE) provides a platform for early professional socialization, potentially affecting the accuracy of stereotypes among health professions students. The purpose of this study was to implement an interprofessional simulation with nursing, respiratory therapy (RT), and speech language pathology (SLP) students, and using the Student Stereotype Rating Questionnaire, evaluate how an IPE simulation approach may alter stereotypes that learners carry with them related to themselves and professions other than their own.Methods and Findings: Participants rated the extent to which they believe attributes, based on nine professional characteristics, apply to either their own profession (autostereotypes), other professions (heterostereotypes), or their own profession as seen by others (perceived autostereotypes) with the Student Stereotype Rating Questionnaire (SSRQ). A quasi-experimental pretest-posttest design was used, and descriptive and analytical statistics conducted within and across groups. Participant impressions of the IPE experience are presented. Main limitations included smaller sample size of RT and SLP participants.Conclusions: Results showed a significant difference from pre- to post-IPE simulation in nursing heterostereoptype, autostereotype, and perceived autostereotype scores. No significant difference was seen in hetereostereotypes of RT and SLP students. Overall, student impressions were positive. Recommendations include study replication for larger sample size.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".