University-level nutrition training in West Africa: cost and financing issues
Bibliographic record
Abstract
BACKGROUND: There is a serious shortage of skilled nutrition professionals in West Africa. Investing in nutrition training is one of the strategies for strengthening the human resource base in nutrition. However, little is known about how nutrition training in the region is financed and the levels of tuition fees charged. The purpose of this study was to provide a comprehensive assessment about the levels of tuition fees charged for nutrition training in the West Africa region and to determine to what extent this is of reach to the average student. METHODOLOGY: The data for this study were obtained from 74 nutrition degree programs operating in nine West African countries in 2013 through semi-structured interviews during on-site visits or through self-administered questionnaires. They included the age of the programs, school ownership, tuition fees, financial assistance, and main sources of funding. Tuition fees (in 2013 US$) were expressed per program to enable uniformity and comparability. Simple descriptive and bivariate analyses were performed. RESULTS: Results from 74 nutrition training programs in nine countries showed a wide variation in tuition fees within and between countries. The tuition fees for bachelor's, master's, and doctoral programs, respectively, ranged from 372 to 4,325 (mean: 2,353); 162 to 7,678 (mean: 2,232); and 369 to 5,600 (mean: 2,208). The tuition fees were significantly higher (p<0.05) in private institutions than in public institutions (mean: US$3,079 vs. US$2,029 for bachelor's programs; US$5,118 vs. US$1,820 for master's programs; and US$3,076 vs. US$1,815 for doctoral programs). The difference in the tuition fees between Francophone and Anglophone countries was not statistically significant (mean: US$2,570 vs. US$2,216 for bachelor's programs; US$2,417 vs. US$2,147 for master's programs; US$3,285 vs. US$2,055 for doctoral programs). In most countries, the tuition fees appeared to be out of reach of the average student. Recent master's programs appeared to charge higher fees than older ones. We found a significant negative correlation between tuition fees and the age of the program, after controlling for school ownership (r=-0.33, p<0.001). CONCLUSIONS: Our findings underscore the urgent need for national governments in the region to establish benchmarks and regulate nutrition training costs. In a region where the average annual gross national income (GNI) per capita is barely 890$, the rising cost of tuition fees is likely to hinder access of students from poor background to nutrition training. Governments should institute financing mechanisms such as scholarships, public-private partnerships, credit facilities, and donor funding to facilitate access to tertiary-level nutrition training in the region.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".