Impact of Interprofessional Education on Collaboration Attitudes, Skills, and Behavior Among Primary Care Professionals
Bibliographic record
Abstract
INTRODUCTION: Care for the frail elderly is often provided by several professionals. Collaboration between them is essential, but remains difficult to achieve. Interprofessional education (IPE) can improve this collaboration. We developed a 9-hour IPE program for primary care professionals from 7 disciplines caring for the frail elderly, and aimed to establish whether the program improved professionals' interprofessional attitudes and attitudes toward collaboration, collaboration skills, and collaborative behavior. We also evaluated learners' reactions to the program. METHODS: Before-after study, using the Interprofessional Attitudes Questionnaire (IAQ, score:1 to 7); Attitudes Toward Health Care Teams Scale (ATHCTS, score: 0 to 105); and Team Skills Scale (TSS, score:17 to 85). Additionally, semistructured interviews were conducted with 10 selected participants. RESULTS: Participants' (N = 80) overall interprofessional attitudes improved (IAQ baseline: 5.49; follow-up: 5.67, p = 0.001); attitudes toward geriatric teams did not change (ATHCTS baseline: 69.9; follow-up: 69.1, p = 0.32). Participants' self-reported team skills improved (TSS baseline: 45.7; follow-up: 48.1, p = 0.001). In the interviews, many interviewees reported increased collaboration with professionals of other disciplines due to the program. Interviewees considered the program's interprofessional nature and attending the program with local professionals important contributing factors to the experienced improvements in collaboration. However, they also noted that not all parts of the program had met the needs of all participating disciplines, due to differences in professional background and knowledge. DISCUSSION: A brief IPE program can improve interprofessional attitudes, collaboration skills, and collaborative behavior. That such a program allows professionals to get acquainted with each other and each other's viewpoints appears to be as important as the educational content.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".