Value of pre-licensure interprofessional education on post-licensure interprofessional collaboration: Perceptions and experiences of practicing professionals
Bibliographic record
Abstract
Interprofessional education (IPE) allows students in health professional programs to practice providing collaborative patient care before graduating. Understanding the perceptions and experiences of health care professionals' IPE received prior to entering the workforce is key for improving IPE programs. This study investigated participants' post-licensure interprofessional collaboration (IPC) experiences, how IPE helped prepare them for IPC post-licensure, their perceptions of the IPE they received as students, and their suggestions for improving IPE. This qualitative descriptive study included 20 healthcare workers from seven professions who graduated from two of three co-located post-secondary educational institutions. Data were collected using semi-structured interviews, which were audiotaped and transcribed verbatim. Inductive thematic analysis revealed five themes and six sub-themes: (a) Quality of care; (b) Role clarification; (c) Interpersonal skills (sub-themes: communication and self-confidence); (d) Co-location; and (e) Need for IPE improvements (sub-themes: additional IPE exposures, shadowing experiences, mandatory IPE, and informal peer learning). These findings appear to reinforce the perception that pre-licensure IPE may support the development of skills for IPC among practicing health professionals.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".