Transfusion medicine and blood banking education and training for blood establishment laboratory staff: A review of selected countries in Africa
Bibliographic record
Abstract
BACKGROUND: Avoidable human error is a significant cause of transfusion adverse events. Adequately trained, laboratory staff in blood establishments and blood banks, collectively blood facilities, are key in ensuring high-quality transfusion medicine (TM) services. Gaps in TM education and training of laboratory staff exist in most African countries. We assessed the status of the training and education of laboratory staff working in blood facilities in Africa. STUDY DESIGN AND METHODS: A cross-sectional study using a self-administered pilot-tested questionnaire was performed. The questionnaire comprised 26 questions targeting six themes. Blood facilities from 16 countries were invited to participate. Individually completed questionnaires were grouped by country and descriptive analysis performed. RESULTS: Ten blood establishments and two blood banks from eight African countries confirmed the availability of a host of training programs for laboratory staff; the majority of which were syllabus or curriculum-guided and focused on both theoretical and practical laboratory skills development. Training was usually preplanned, dependent on student and trainer availability and delivered through lecture-based classroom training as well as formal and informal on the job training. There were minimal online didactic and self-directed learning. Teaching of humanistic values appeared to be lacking. CONCLUSION: We confirmed the availability of diverse training programs across a variety of African countries. Incorporation of virtual learning platforms, rather than complete reliance on didactic, in-person training programs may improve the education reach of the existing programs. Digitalization driven by the coronavirus disease 2019 pandemic may provide an opportunity to narrow the knowledge gap in low- and middle-income countries (LMICs).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".