Strengthening and expanding the capacity of health worker education in Zambia
Bibliographic record
Abstract
INTRODUCTION: Zambia is facing a chronic shortage of health care workers. The paper aimed at understanding how the Medical Education Partnership Initiative (MEPI) program facilitated strengthening and expanding of the national capacity and quality of medical education as well as processes for retaining faculty in Zambia. METHODS: Data generated through documentary review, key informant interviews and observations were analyzed using a thematic approach. RESULTS: The MEPI program triggered the development of new postgraduate programs thereby increasing student enrollment. This was achieved by leveraging of existing and new partnerships with other universities and differentiating the old Master in Public Health into specialized curriculum. Furthermore, the MEPI program improved the capacity and quality of training by facilitating installation and integration of new technology such as the eGranary digital library, E-learning methods and clinical skills laboratory into the Schools. This technology enabled easy access to relevant data or information, quicker turn around of experiments and enhanced data recording, display and analysis features for experiments. The program also facilitated transforming of the academic environment into a more conducive work place through strengthening the Staff Development program and support towards research activities. These activities stimulated work motivation and interest in research by faculty. Meanwhile, these processes were inhibited by the inability to upload all courses on to Moodle as well as inadequate operating procedures and feedback mechanisms for the Moodle. CONCLUSION: Expansion and improvement in training processes for health care workers requires targeted investment within medical institutions and strengthening local and international partnerships.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".