Stakeholders’ perceptions of the nutrition and dietetics needs and the requisite professional competencies in Uganda: a cross-sectional mixed methods study
Bibliographic record
Abstract
BACKGROUND: Effective implementation of nutrition and dietetics interventions necessitates professionals in these fields to possess the requisite competencies for health systems performance. This study explored the stakeholders' perceptions of the community nutrition and dietetics needs, the nature of work done by graduates of the Bachelor's degree in Human Nutrition/Human Nutrition and Dietetics (HN/HND), and the competencies required of Nutrition and Dietetics professionals in Uganda. METHODS: A cross-sectional mixed methods design was used. Respondents included 132 graduates of the Bachelor's degrees in HN/HND obtained from the Makerere and Kyambogo Universities in 2005-2016; 14 academic staff that train HN/HND in the two universities; and 11 HN/HND work/internship supervisors. Data from the graduates was collected through an email-based survey; data from other participants was through face to face interviews using researcher administered questionnaires. RESULTS: Most HN/HND respondents (84.8%) obtained their Bachelor's degrees from Kyambogo University; 61.4% graduated in 2013-2016. Most (64.3%) academic staff respondents were females and the majority (57.1%) had doctorate training. All stakeholders viewed communities as facing a variety of nutrition and dietetics challenges cutting across different Sustainable Development Goals. The nutrition and dietetics interventions requested for, provided, and considered a priority for communities were both nutrition-specific and nutrition-sensitive. Work done by HN/HND graduates encompassed seven main competency domains; the dominant being organizational leadership and management; management of nutrition-related disease conditions; nutrition and health promotion; research; and advocacy, communication, and awareness creation. CONCLUSIONS: This study shows that nutrition and dietetics challenges in Uganda are multiple and multifaceted; HN/HND graduates are employed in different sectors, provide nutrition-specific and sensitive services in a multisectoral environment, and are expected to possess a variety of knowledge and skills. However, graduates have knowledge and skills gaps in some of the areas they are expected to exhibit competency. We recommend using these findings as a basis for obtaining stakeholder consensus on the key competencies that should be exhibited by all HN/HND graduates in Uganda; developing a HN/HND competency-based education model and a national HN/HND training and practice standard; and undertaking further research to understand the quality and relevancy of HN/HND curricula to Uganda's job market requirements.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".