Knowledge, attitude and practice survey of bacterial contamination of blood for transfusion in the Democratic Republic of the Congo.
Bibliographic record
Abstract
BACKGROUND: We assessed healthcare worker's knowledge-attitude-practice regarding bacterial contamination of blood products in the Democratic Republic of the Congo. MATERIALS AND METHODS: In three hospitals and the National Blood Transfusion Center (NBTC), two multiple-choice surveys were completed on a tablet computer: one each, for blood bank (31 questions) and for clinical ward staff (20 questions). A score was calculated for 11 overlapping knowledge questions. RESULTS: Among 247 participants (blood bank No.=62, ward No.=185), median (range) knowledge score was 10 (2-19) on a maximum of 20, with blood bank staff (12/20) scoring higher than clinical ward staff (9/20) (p<0.0001). Half (50.2%) of 247 participants recalled previous training in transfusion medicine. Participants had limited understanding of and compliance with NBTC-recommended preventive measures: incorrect assumption that wearing gloves prevents bacterial contamination (83.8%) and that blood banks test donor blood for bacteria (59.9%). Half (50.0%) of blood bank staff did not acknowledge the NBTC-recommended antisepsis procedure, 62.1% did not apply the appropriate number of antisepsis steps, and 32.3% saw no harm in touching the venipuncture site after antisepsis. Presence of bacteria on healthy skin (62.3%) and blood bank fomites (examination gloves: 30.8%, soap: 62.8%) was underestimated. Although 92.4% of clinical ward staff said to easily recognize transfusion reactions, only 15.7% recognized septic reactions and post-transfusion antibiotic treatment practices were not consistent. Challenges reported by blood bank staff and particular for low-resource settings were: frequent power cuts (98.4%), transport of blood products by patient attendants (41.1%), without cooling elements (64.4%), and reuse of finished antiseptic/disinfectant containers (75.4%). DISCUSSION: The present study points to gaps in knowledge, attitudes, practices along sampling, cold chain and transfusion which can feed customized training and monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".