Validation of the interprofessional collaborative competency attainment survey (ICCAS)
Bibliographic record
Abstract
The purpose of this study was to obtain evidence regarding the validity and reliability of an instrument to measure the self-reported competencies of interprofessional care in interprofessional education programs. Five hundred and eighty-four students and clinicians in Canada and New Zealand who were registered in 15 interprofessional education undergraduate, postgraduate, and continuing professional development programs completed the Interprofessional Collaborative Competency Attainment Survey (ICCAS) using a retrospective pre-test/post-test design. Factor analyses showed the presence of two factors in the pre-program items and one factor in the post-program items. Tests conducted provided evidence in support of the validity and reliability of the ICCAS as a self-assessment instrument for interprofessional collaborative practice. Internal consistency was high for items loading on factor 1 (α = 0.96) and factor 2 (α = 0.94) in the pre-program assessment and for the items in the post-program assessment (α = 0.98). The transition from a two factor solution to a single factor structure suggests interventions influence learners' understanding of interprofessional care by promoting the recognition of the high degree of interrelation among interprofessional care competencies. Scores on the ICCAS are reliable and predict meaningful outcomes with regard to attitudes toward interprofessional competency attainment.
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 | 0.034 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".