Development of a scale to measure health professions students' self-efficacy beliefs in interprofessional learning
Bibliographic record
Abstract
A need exists for measures to evaluate the impact of interprofessional education (IPE) interventions. We undertook development and evaluation of a scale to measure self-efficacy perceptions of pre-licensure students in medicine, dentistry and health professions. The scale was developed in the context of a project entitled, "Seamless Care: An Experiential Model of Interprofessional Education for Collaborative Patient-Centered Practice". As self-efficacy perceptions are associated with the likelihood of taking on certain tasks, the difficulty of those tasks, and perseverance in the face of barriers, we reasoned that understanding changes in students' perceptions and their relation to other outcomes was important. A 16-item scale was developed from a conceptual analysis of relevant tasks and the existing literature. Content validity was assessed by six Canadian IPE experts. Pre-licensure students (n = 209) participated in a pilot test of the instrument. Content validity was rated highly by the six judges; internal consistency of the scale (Cronbach's α = 96) and subscales 1 (α = .94) and 2 (α = .93) were high. Principal components analysis resulted in identification of two factors, each accounting for 34% of the variance: interprofessional interaction, and interprofessional team evaluation and feedback. We conclude that this scale can be useful in evaluating IPE interventions.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".