Designing and Operationalizing a Toolkit of Bilingual Interprofessional Education Assessment Instruments
Bibliographic record
Abstract
This article addresses one of the most important unresolved issues of interprofessional education (IPE): assessment. Here we describe our process and experiences designing and operationalizing a toolkit of qualitative and quantitative IPE assessment instruments for online and face-to-face education programs developed concurrently in both English and French. The toolkit includes a) the quantitative W(e)Learn program evaluation survey, which aligns with the W(e)Learn framework, b) the quantitative Interprofessional Collaborative Competencies Attainment Survey (ICCAS), to self-assess competency development in collaborative practice using a post-post design, and c) qualitative team and learner contracts, with explanatory exemplars, that serve as both learning and assessment tools. These instruments are currently undergoing validation in hopes of a) increasing the likelihood that IPE experiences are planned and delivered effectively and b) increasing the justification and accountability of IPE experiences and practical outcomes. Although this validation process will continue for some time, the development of the IPE assessment tools is worthy of particular attention in order to guide further work in this field. French and English copies of the toolkit assessments can be downloaded from http://ennovativesolution.com/WeLearn/IPE-Instruments.html. Although these instruments were designed with interprofessional healthcare teams in mind, we feel they could readily be transferable to a variety of interdisciplinary tasks and settings, such as social work and human services education.
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 | 0.144 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".