Continuing Education of Animal Health Professionals in Uganda: A Training Needs Assessment
Bibliographic record
Abstract
In Uganda, delivery of veterinary services is vital to animal health and productivity, and is heavily dependent on well-trained and skilled animal health professionals. The purpose of this study was to identify and prioritize areas for refresher training and continuous professional development of animal health professionals (veterinarians and veterinary paraprofessionals), with the overarching aim of improving veterinary service delivery in Uganda. A survey was administered electronically to 311 animal health professionals during the period November 14-30, 2019. Data were collected on relevant parameters including demographics, knowledge on preventive medicine, diagnostics, disease control and treatment, epidemiology, and One Health, as well as participants' opinions on training priorities, challenges faced, and constraints to veterinary service delivery. Most respondents were veterinarians 26-35 years old, were male, and worked in clinical practice. Lowest perceived knowledge was reported on subjects relating to laboratory diagnostics, antimicrobial resistance (AMR), and nutrition. Training topics considered to be of most benefit to respondents included laboratory diagnostics, treatment of common livestock diseases, AMR, and practical clinical skills in reproductive and preventive medicine. Participants preferred to receive training in the form of practical workshops, in-practice training, and external training. This study highlights the need to prioritize training in practical clinical skills, laboratory diagnostics, and AMR. Wet labs and hands-on practical clinical and laboratory skills should be incorporated to enhance training. Provision of targeted and successful trainings will be dependent on the allocation of adequate resources and support by relevant public and private stakeholders across the veterinary sector.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".