Healthcare Student Stereotypes: A Systematic Review with Implications for Interprofessional Collaboration
Bibliographic record
Abstract
Background: Stereotyping is one factor theorized to facilitate or inhibit effective interprofessional healthcare education and collaboration. The primary purpose of this paper is to systematically review the literature to determine what stereotypes are present among healthcare students about other healthcare students and practitioners. The secondary purpose of this paper is to identify the instruments most commonly used to measure stereotypes held by healthcare practitioners and students. Methods and Findings: A search of nine electronic databases identified studies that examined stereotypes among healthcare students. Studies were included if they met three search criteria: utilized quantitative methods; collected data on the stereotypes of healthcare students, including medical students, toward other healthcare students or healthcare practitioners; and included participants who were enrolled in a professional healthcare program. Thirteen studies were identified for this review. The results demonstrate that students of various healthcare professions hold stereotypes characterized by both positive and negative adjectives of students and practitioners in their own and other healthcare professions. Conclusions: The presence of stereotypes among students may have an influence on patterns of communication and collaboration during future practice in the healthcare environment. Key Words: Stereotypes, Interprofessional, Healthcare Students, Healthcare Education
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".