Evaluation of a Simulation-Based Interprofessional Educational Module on Adult Suctioning Using Action Research
Bibliographic record
Abstract
AbstractBackground: Interprofessional collaboration in healthcare contributes to patientwell-being. The purpose of this action research study was to evaluate an innovative interprofessional simulation educational module for pre-licensure healthcare students on adult suctioning skills.Methods and Findings: Two suctioning scenarios were developed to provide multiprofessional groups of students the opportunity to collaborate in applying basicsuctioning skills within complex patient care situations. One group of nursing students (N= 23) and one group of physiotherapy students (N= 23) learned suctioning skills uniprofessionally in the usual programs of their respective schools. A third group of students (N= 45; 21 nursing, 24 physiotherapy) learned suctioning in the new, interprofessional simulation-based curriculum. Qualitative data were collected through direct observation of laboratory sessions, open-ended surveys, and focus groups. A thematic qualitative analysis was conducted, and four major themes emerged: instructors’ role expectations, prior student learning, student collaboration, and instructor communication. In addition, quantitative analysis of students’ readiness for interprofessional collaboration, confidence, and performance of suctioning skills revealed no significant differences between learners in the interprofessional labs and those in the uniprofessional labs.Conclusions: Development of the educational module using action research allowed for further development of a pedagogical approach to interprofessional education to increase its effectiveness.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".