Operationalization of bandura’s social learning theory to guide interprofessional simulation
Bibliographic record
Abstract
Background and objective: In the clinical setting, health care professionals are expected to work in teams, yet, there is limited academic exposure to other allied health students and little preparation is done in traditional classrooms to practice with other allied health students. As health professionals work in an environment influenced by social interaction, interprofessional simulation (IPS) instruction may lack necessary frameworks that support professional practice. To promote collaborative learning in IPS that takes into account real interprofessional clinical situations, Bandura’s social learning theory was used as the guiding framework for this pilot simulation study.Methods: Conventional content analysis, as used in study designs to describe a phenomenon, allowed for the flow of categories to be derived from standardized debriefing sessions with nursing and social work students (N = 24).Results: Qualitative data identified three themes capturing students’ voices: effective and efficient patient care, team appreciation, and early implementation of simulation.Discussion and conclusions: Outcomes of this pilot study support the integration of a guiding framework in designing IPS for nursing and social work education that takes into account the social nature of the clinical environment through observed action and replicated behavior for requisite interprofessional skills for clinical practice.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".