Becoming Interprofessional: A Longitudinal Study of Professional and Interprofessional Identity Development Across Five Health Professions
Bibliographic record
Abstract
Interprofessional collaborative practice (IPC) occurs when health professions work collaboratively to improve quality of care and enhance patient outcomes. Yet myriad challenges to enacting collaborative practice exist. Interprofessional education for collaborative practice (IPECP) is foundational for promoting collaboration among health professions, yet there is a gap in understanding how students perceive their readiness for IPC and how early socialization experiences may contribute to developing a dual-uni-professional and interprofessional-identity. This study seeks to understand how new practitioners perceive and experience IPC upon entry to practice, and identify individual and systemic factors that facilitate and impede dual identity development. An interpretive, narrative methodology was used to understand the IPC and early professional practice experiences of 24 individuals from a longitudinal study of five health professions. Facilitators to interprofessional identity development included exposure to/working with interprofessional teams, settings, role models, and directly experiencing benefits of collaborative practice during patient care. Impediments include settings and situations where professional stereotyping and hierarchies were reinforced by the dominant uni-professional culture of work environments. Interprofessional socialization and identity development are contingent on exposure to interprofessional role models and settings. Healthcare professionals' dual identity development begins in pre-licensure IPECP but is shaped by socialization experiences within practice. Healthcare institutions need to provide nourishing collaborative environments (time, settings, and contexts) that foster interprofessional collaboration and behaviors and empower dual identity formation. Post-licensure IPECP for healthcare professionals to continue to learn with, from, and about one another in practice is essential for collaborative interprofessional healthcare teams/systems.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".