Crucial Conversations: An interprofessional learning opportunity for senior healthcare students
Bibliographic record
Abstract
Clinical errors due to human mistakes are estimated to result in 400,000 preventable deaths per year. Strategies to improve patient safety often rely on healthcare workers' ability to speak up with concerns. This becomes difficult during critical decision-making as a result of conflicting opinions and power differentials, themes underrepresented in many interprofessional initiatives. These elements are prominent in our interprofessional initiative, namely Crucial Conversations. We sought to evaluate this initiative as an interprofessional learning (IPL) opportunity for pre-licensure senior healthcare students, as a way to foster interprofessional collaboration, and as a method of empowering students to vocalise their concerns. The attributes of this IPL opportunity were evaluated using the Points for Interprofessional Education Score (PIPES). The University of the West of England Interprofessional Questionnaire was administered before and after the course to assess changes in attitudes towards IPL, relationships, interactions, and teamwork. Crucial Conversations strongly attained the principles of interprofessional education on the PIPES instrument. A total of 38 volunteers completed the 16 hours of training: 15 (39%) medical rehabilitation, 10 (26%) medicine, 7 (18%) pharmacy, 5 (13%) nursing, and 1 (2%) dentistry. Baseline attitude scores were positive for three of the four subscales, all of which improved post-intervention. Interprofessional interactions remained negative possibly due to the lack of IPL opportunities along the learning continuum, the hidden curriculum, as well as the stereotyping and hierarchical structures in today's healthcare environment preventing students from maximising the techniques learned by use of this interprofessional initiative.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".