Higher Education Institution Partnership to Strengthen the Health Care Workforce in Afghanistan
Bibliographic record
Abstract
Despite ongoing insecurity, Afghanistan has demonstrated improvement in health outcomes. Reasons for this success include a strategic public-private health service delivery model and investment in Afghan health care workforce development. Afghan universities have the primary responsibility for ensuring that an adequate health care workforce is available to private and public health care delivery settings. Most entry-level health care providers working in Afghanistan are educated within the country. However, university constraints, including faculty shortages and limited access to professional development, have affected both the flow of the health care workforce pipeline and the skill levels and competencies of those who do enter the workforce. Aware of these constraints and workforce needs, the administration at Kabul University of Medical Sciences (KUMS), working in collaboration with the Ministry of Higher Education, prioritized investment in strengthening technical and academic capabilities within four faculties (anesthesiology, dentistry, medical laboratory technology, and midwifery). KUMS partnered with the University of Minnesota in 2017 with United States Agency for International Development support through the University Support and Workforce Development Program. Together they established a unique training-of-trainers (TOT) faculty development program to improve faculty knowledge and skills specific to their technical expertise, as well as knowledge and skills in instructional design and research methods. In this article, we describe the successes and challenges associated with partnership development, implementation, and sustainability.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".