Current and Future Needs for Human Resources for Ethiopia’s National Health Information System: Survey and Forecasting Study
Bibliographic record
Abstract
BACKGROUND: Strengthening the national health information system is one of Ethiopia's priority transformation agendas. A well-trained and competent workforce is the essential ingredient to a strong health information system. However, this workforce has neither been quantified nor characterized well, and there is no roadmap of required human resources to enhance the national health information system. OBJECTIVE: We aimed to determine the current state of the health information system workforce and to forecast the human resources needed for the health information system by 2030. METHODS: We conducted a survey to estimate the current number of individuals employed in the health information system unit and the turnover rate. Document review and key-informant interviews were used to collect current human resources and available health information system position data from 110 institutions, including the Ministry of Health, federal agencies, regional health bureaus, zonal health departments, district health offices, and health facilities. The Delphi technique was used to forecast human resources required for the health information system in the next ten years: 3 rounds of workshops with experts from the Ministry of Health, universities, agencies, and regional health bureaus were held. In the first expert meeting, we set criteria, which was followed by expert suggestions and feedback. RESULTS: As of April 2020, there were 10,344 health information system professionals working in the governmental health system. Nearly 95% (20/21) of district health offices and 86.7% (26/30) of health centers reported that the current number of health information system positions was inadequate. In the period from June 2015 to June 2019, health information technicians had high turnover (48/244, 19.7%) at all levels of the health system. In the next ten years, we estimate that 50,656 health information system professionals will be needed to effectively implement the Ethiopia's national health information system. CONCLUSIONS: Current health information system-related staffing levels were found to be inadequate. To meet the estimated need of 50,656 multidisciplinary health information system professionals by 2030, the Ministry of Health and regional health bureaus, in collaboration with partners and academic institutions, need to work on retaining existing and training additional health information system professionals.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".