Scarcity of protective items against HIV and other bloodborne infections in 13 low‐ and middle‐income countries
Bibliographic record
Abstract
OBJECTIVE: To assess protection of surgical healthcare workers against HIV and other bloodborne infections in low- and middle-income countries (LMICs). METHODS: Literature review based on recent studies assessing baseline surgical capacity in LMICs using the WHO Situational Analysis of Access to Emergency and Essential Surgical Care, the Surgeons OverSeas (SOS) Personnel, Infrastructure, Procedures, Equipment and Supplies (PIPES) survey and the Harvard Humanitarian Initiative survey tools. The availability of protective eyewear, sterile gloves and sterilisers was assessed. RESULTS: Thirteen individual country studies with relevant data were identified documenting items from 399 hospitals. The countries included Afghanistan, Bolivia, Gambia, Ghana, Liberia, Mongolia, Nigeria, Sierra Leone, Solomon Islands, Somalia, Sri Lanka, Tanzania and Zambia. Overall, only 29% (79/270) of hospitals always had eye protection. Sterilisers were only available at 64% (244/383) of facilities. Sterile gloves were the most available item, available at 75% of facilities (256/340). CONCLUSION: Surgical healthcare worker protection for bloodborne infections continues to be deficient in LMICs. Improved documentation of these items should be incorporated into future surgical capacity studies. Policy makers and clinicians should work together to secure resources and interventions that will protect this vital workforce.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".