Psychometric validity of the interprofessional professionalism assessment assessment instrument in nursing education: Short report
Bibliographic record
Abstract
The demand for valid and reliable instruments to assess behaviors in interprofessional education (IPE) opportunities coincides with the recent shift in nursing education toward competency-based education (CBE). The purpose of the proposed study is to validate the Interprofessional Professionalism Assessment (IPA) instrument within the context of nursing education. The IPA instrument is used to assess individual interprofessional professionalism (IPP) in IPE and consists of 26 items clustered across six subscales: Accountability, Altruism and Caring, Communication, Ethics, Excellence, and Respect. The proposed study will be among the first to involve psychometric testing of the IPA instrument among the health professions and the first in nursing education, building upon the foundational work of the Interprofessional Professionalism Collaborative. The aim is to fill the gap in robust assessment tools that measure professionalism within IPE opportunities in a CBE framework. Potential participants for this study include clinical instructors overseeing prelicensure nursing students in the final year of their programs, ensuring a diverse representation of nursing students. The study will have a non-experimental, cross-sectional, and correlational design involving a web-based survey created with Qualtrics to validate the IPA instrument and explore the relationship between the IPA instrument and the Individual Teamwork Observation and Feedback Tool (i-TOFT). Confirmatory factor analysis will reveal the interrelationships among the items of the IPA instrument by investigating factor retention.
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 | 0.030 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".