The status of interprofessional education (IPE) at regional and global levels – update from 2022 global IPE situational analysis
Bibliographic record
Abstract
This short report is based on the 2022 Global IPE Situational Analysis Results e-Book that is available at https://interprofessionalresearch.global/. As an up-to-date global environmental scan of interprofessional education (IPE), this cross-sectional study investigated institutional, administrative, and system-level processes that support IPE program development and implementation globally. Conducted by InterprofessionalResearch.Global (IPR.Global), the survey included 17 quantitative questions that were analyzed at global and regional levels. Three open-text questions were thematically analyzed. In total, 152 institutions from six regions worldwide contributed to this study. Results revealed that only 51.97% of all responding institutions have an established IPE program, with Canada and the USA having the highest (84%) and Africa (26%) having the lowest numbers. Globally, 37.33% of respondents reported no formal leadership positions and 41.33% reported the absence of a designated IPE Director or Coordinator. In addition, IPE funding varies considerably across the world, with 32.65% of institutions reporting no financial support. Over 48.22% of respondents indicated their institutions are rarely or not involved in IPE-related scholarly work or research. The open-text analysis revealed that supportive senior leadership, a culture of collaboration, and recognition of IPE as a strategic direction and/or priority at the institutional level, could foster the successful implementation of IPE. On the other hand, inadequate administrative support, lack of funding, poor attitudes regarding IPE, and limited dedicated time for research, seemed to impair successful implementation of scholarly activities in the field.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".