Developing pre-licensure interprofessional and stroke care competencies through skills-based simulations
Bibliographic record
Abstract
Interprofessional collaboration (IPC) in stroke care is accepted as best practice and necessary given the multi-system challenges and array of professionals involved. Our two-part stroke team simulations offer an intentional interprofessional educational experience (IPE) embedded in pre-licensure occupational therapy, physical therapy, pharmacy, medicine, nursing and speech-language pathology curricula. This six-year mixed method program evaluation aimed to determine if simulation delivery differences necessitated by COVID-19 impacted students’ IPC perception, ratings, and reported learning. Following both simulations, the Interprofessional Collaborative Competency Assessment Scale (ICCAS) and free-text self-reported learning was voluntarily and anonymously collected. A factorial ANOVA using the ICCAS interprofessional competency factors compared scores across delivery methods. Content and category analysis was done for free-text responses. Overall, delivery formats did not affect positive changes in pre-post ICCAS scores. However, pre and post ICCAS scores were significantly different for interprofessional competencies of roles/responsibilities and collaborative patient/family centered approach. Analysis of over 10,000 written response to four open-ended questions revealed the simulation designs evoked better understanding of others’ and own scope of practice, how roles and shared leadership change based on context and client need, and the value of each team member’s expertise. Virtual-experience-only students noted preference for an in-person stroke clinic simulation opportunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".