International collaboration to protect health workers from infectious diseases in Ecuador
Bibliographic record
Abstract
The Healthy Hospital Project, an international collaboration, aimed to strengthen Ecuador's capacity to promote healthier and safer hospitals by reducing occupational transmission of infectious diseases. Team members conducted a needs assessment to identify workplace hazards and health risks in three hospitals. A survey of health care workers' knowledge and practices of occupational health (OH) and infection control (IC) revealed positive practices such as a medical waste disposal program and widespread dissemination of health information. Challenges identified included a high frequency of recapping needles and limited resources for workers to apply consistent IC measures. The survey revealed underreporting of needlestick injuries and limited OH and safety (OHS) training. Therefore, project collaborators organized a training workshop for health care workers that aimed to overcome the identified obstacles by integrating interdisciplinary local, national, and international stakeholders to build capacity and institutionalize work-related infection prevention and control measures. The knowledge transferred and experience gained led to useful hospital-based projects and serves as a basis for implementation of other OHS projects nationwide. International interdisciplinary, interinstitutional collaboration in OHS and IC can build capacity to address OHS concerns in health care.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".