Addressing health workforce shortages as a precursor to attaining universal health coverage: A comparative policy analysis of Nigeria and Ghana
Bibliographic record
Abstract
There is a critical shortage of health professionals globally which is affecting the possibility of attaining universal health coverage. Developing countries in sub-Saharan Africa such as Ghana and Nigeria are disproportionately affected and the shortfall in health professionals is envisaged to worsen over the next decade. Countries have responded differently in addressing this shortage. To understand the differing response to the same policy issue in two countries that share similar characteristics in terms of geolocation, socioeconomic indices and disease burden, this paper offers a comparative policy analysis of the two countries using the 3-I framework and punctuated equilibrium theory as comparative policy analysis tools. The analysis identified the ideas, interests, and institutions at play and how they have led to different policy outcomes in both countries. The analysis also shows the interaction between subsystems, policy images and policy venues and how this interaction led to policy change, in the case of Ghana and lag in the case of Nigeria. Our findings show four critical areas in addressing health workforce shortages in both countries - a general approach to addressing the issue, welfare and remuneration, workforce autonomy and career progression, and financing for workforce improvement. For Ghana, there has been significant policy change including implementing strategies for increasing the production of health professionals and addressing remuneration and welfare issues. For Nigeria, there has been seems to be a lag in policy change. While the findings show that Ghana's approach has seemingly put them on a good path toward universal health coverage, applying any lessons should, however, be contextual, considering other country-level and health systems factors that are relevant to addressing health workforce shortages.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".