Seeing is believing: an educational outreach activity on disinfection practices
Bibliographic record
Abstract
BACKGROUND: Skin and soft-tissue infections are very common among persons who inject drugs. They occur when microbes pass under the protective layer of the skin and proliferate. This happens when harm reduction recommendations such as skin aseptia before injection and sterile injection equipment usage are not properly followed. METHODS: A group of active drug users involved in a health promotion project as peer educators were asked about their formation needs. To address their inquiries concerning skin and soft-tissue infections, we devised with them a series of workshops touching upon common infections, the microflora, and microbial transmission. RESULTS: Participants learned to identify common infections and how to properly react in case of an abscess, cellulitis or phlebitis. They saw microscopic objects, found out about the high prevalence of microbes in their environment and on their skin, and could appreciate the efficiency of different washing and disinfection techniques. They visualized how easily microbes can spread from person to person and from contaminated objects to persons. CONCLUSION: In the weeks following this activity, some participants demonstrated and reported healthy behavioural changes regarding their own injection practices. Furthermore, they shared their newfound knowledge and began enforcing its application among people they inject drugs with. Most participants greatly appreciated this activity and valued it as being highly efficient and tangible. Note: A French version of this paper is available on the Journal's web site [see Additional file 1].
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".