Refinement of the Interprofessional Socialization and Valuing Scale (ISVS-21) and Development of 9-Item Equivalent Versions
Bibliographic record
Abstract
INTRODUCTION: Measures of interprofessional (IP) socialization are needed to capture the role of interprofessional education in preparing students and health practitioners to function as part of IP health care teams. The aims of this study were to refine a previously published version of the Interprofessional Socialization and Valuing Scale (the ISVS-24) and create two shorter equivalent forms to be used in pre-post studies. METHODS: A graded response model was used to identify ISVS items in a practitioner data set (n = 345), with validation (measure invariance) conducted using a separate student sample (n = 341). RESULTS: Analyses indicated a unidimensional 21-item version with excellent measurement properties, Cronbach alpha of 0.988, 95% confidence interval (CI) 0.985-0.991. There was evidence of measure invariance, as there was excellent agreement of the factor scores for the practitioner and student data, intraclass correlation coefficient = 0.993, 95% CI 0.991-0.994. This indicates that the ISVS-21 measures IP socialization consistently across groups. Two 9-item equivalent versions for pre-post use were developed, with excellent agreement between the two forms. The student score agreement for the two item sets was excellent: intraclass correlation coefficient = 0.970, 95% CI 0.963-0.976. DISCUSSION: The ISVS-21 is a refined measure to assess existing levels of IP socialization in practitioners and students, and relate IP socialization to other important constructs such as IP collaboration and the development of an IP identity. The equivalent versions can be used to assess change in IP socialization as a result of interprofessional education.
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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 | 0.011 | 0.030 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".