International consensus statement on the assessment of interprofessional learning outcomes
Bibliographic record
Abstract
Regulatory frameworks around the world mandate that health and social care professional education programs graduate practitioners who have the competence and capability to practice effectively in interprofessional collaborative teams. Academic institutions are responding by offering interprofessional education (IPE); however, there is as yet no consensus regarding optimal strategies for the assessment of interprofessional learning (IPL). The Program Committee for the 17th Ottawa Conference in Perth, Australia in March, 2016, invited IPE champions to debate and discuss the current status of the assessment of IPL. A draft statement from this workshop was further discussed at the global All Together Better Health VIII conference in Oxford, UK in September, 2016. The outcomes of these deliberations and a final round of electronic consultation informed the work of a core group of international IPE leaders to develop this document. The consensus statement we present here is the result of the synthesized views of experts and global colleagues. It outlines the challenges and difficulties but endorses a set of desired learning outcome categories and methods of assessment that can be adapted to individual contexts and resources. The points of consensus focus on pre-qualification (pre-licensure) health professional students but may be transferable into post-qualification arenas.
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 | 0.252 | 0.237 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".