Bibliographic record
Abstract
, conducted using the Walker and Avant approach. Our literature search resulted in 27 publications with meaningful insights regarding interprofessional socialization, published between 1994-2022. We identified five defining attributes of interprofessional socialization: (a) learning about other professionals and the roles they play on interprofessional teams, (b) recognizing the value of collaborating with other professionals, (c) identifying a common goal shared across professions, (d) breaking down barriers between professional silos, and (e) developing dual identity: a professional identity and an interprofessional identity. We identified antecedents, consequences, empirical referents, and cases to better illustrate the concept. Insights from this concept analysis provided the foundation for a conceptual definition. Interprofessional socialization is an iterative process in which members from different professions come together to learn about and value each other's perspectives and contributions, while dispelling misconceptions and prejudices, continuously working toward formation of a dual identity: one for professional identity and one for interprofessional identity. Future research is needed to explore how interprofessional socialization changes over the course of a career and how efforts to increase interprofessional socialization across healthcare settings might impact interprofessional initiatives throughout healthcare 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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.035 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".