Impact of an interprofessional education session with healthcare professionals on attitudes towards interprofessional teams
Bibliographic record
Abstract
There is a widespread emphasis in healthcare delivery on the need for interprofessional collaborative care in order to enhance quality and safety in patient care. Interprofessional learning about collaboration, problem solving and decision-making beyond the confines of individual professions is important for practicing health professionals. As attitude has been found to predict behaviour, a positive attitude by health professionals toward interprofessional teams could positively affect interprofessional team functioning, and subsequently the quality of care provided to the patient. The purpose of this study was to determine whether an interprofessional learning experience would improve attitudes toward interprofessional teams using the Attitudes Toward Health Care Teams Scale. Healthcare professionals attending a new employee orientation completed the Attitudes Towards Health Care Teams (ATHCT) scale before and after an interprofessional education intervention. Results revealed a statistically significant increase in ATHCT scale mean score following the interprofessional education intervention. Findings from this study suggest that interprofessional learning can be an effective means to increase attitudes toward interprofessional teams and potentially contribute to improving interprofessional collaboration in healthcare.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".