Integrating interprofessional education with needs-based health workforce planning to strengthen health systems
Bibliographic record
Abstract
Providing quality health care is the core purpose for health systems, and it is only possible with adequate capacity among the workforce to provide the required services. Addressing the requirements for, and supply of, the health workforce (workforce planning) is essential for strengthening health systems. There is a global recognition that interprofessional education (IPE) is critical to achieving universal health care. In this introductory paper we discuss how IPE is a key factor within needs-based health systems strengthening and Human Resources for Health (HRH) planning. This perspective is illustrated through six case studies from countries around the globe which provide discourse on how the integration of IPE/IPC with needs-based workforce planning can contribute to strengthening the health systems. Three key learnings arise from the case studies - 1) IPE is important to meet health care needs of populations efficiently and effectively; 2) integrated needs-based planning provides a framework within which IPE has an integral role, and 3) stakeholders from both health and education are critical to the process of seamless integration of IPE across the continuum of health systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".