Attitudes of health sciences faculty members towards interprofessional teamwork and education
Bibliographic record
Abstract
OBJECTIVES: Faculty attitudes are believed to be a barrier to successful implementation of interprofessional education (IPE) initiatives within academic health sciences settings. The purpose of this study was to examine specific attributes of faculty members, which might relate to attitudes towards IPE and interprofessional teamwork. METHODS: A survey was distributed to all faculty members in the medicine, nursing, pharmacy and social work programmes at our institution. Respondents were asked to rate their attitudes towards interprofessional health care teams, IPE and interprofessional learning in an academic setting using scales adopted from the peer-reviewed literature. Information on the characteristics of the respondents was also collected, including data on gender, prior experience with IPE, age and years of practice experience. RESULTS: A total response rate of 63.0% was achieved. Medicine faculty members reported significantly lower mean scores (P < 0.05) than nursing faculty on attitudes towards IPE, interprofessional teams and interprofessional learning in the academic setting. Female faculty and faculty who reported prior experience in IPE reported significantly higher mean scores (P < 0.05). Neither age, years of practice experience nor experience as a health professional educator appeared to be related to overall attitudinal responses towards IPE or interprofessional teamwork. CONCLUSIONS: The findings have implications for both the advancement of IPE within academic institutions and strategies to promote faculty development initiatives. In terms of IPE evaluation, the findings also highlight the importance of measuring baseline attitudinal constructs as part of systematic evaluative activities when introducing new IPE initiatives within academic settings.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".