Outcome of a One-Week Intensive Training Workshop for Veterinary Diagnostic Laboratory Workers in Liberia
Bibliographic record
Abstract
There is a huge unmet need for veterinary diagnostic laboratory services in developing nations such as Liberia. One way of bridging the service gap is for visiting experts to provide veterinary laboratory training to technicians in a central location in a short-course format. An intensive 1-week training workshop was organized for 18 student and faculty participants from the College of Agriculture and Integrated Development Studies (CAIDS) at Cuttington University in rural central Liberia. The training was designed and delivered by the non-governmental organization Veterinarians Without Borders US and funded through a Farmer-to-Farmer grant provided by the United States Agency for International Development. Although at the start of training none of the students had any veterinary laboratory experience, by the end of the course over 80% of the students were able to discuss appropriate care and use of a microscope and name at least three important components of laboratory record keeping; over 60% were able to describe how to make and stain a blood smear and how to perform a passive fecal flotation; and over 30% were able to describe what a packed cell volume is and how it is measured and name at least three criteria for classifying bacteria. The intensive training workshop greatly improved the knowledge of trainees about veterinary diagnostic laboratory techniques. The training provided initial skills to students and faculty who are awaiting the arrival of additional grant-funded laboratory equipment to continue their training.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".