A narrative review of infections associated with personal service establishments Part I: Aesthetics
Bibliographic record
Abstract
Personal service establishments (PSEs) offer a range of services to their clientele, including manicures, pedicures, facials, waxing, and hairstyling and (or) barbering services. Several key gaps exist with respect to regulations, guidelines, and best practices for PSEs. One major gap is the lack of information on infection risks associated with PSEs. To address this, we conducted a review of the scientific literature to investigate the relationship between bacterial, viral, and fungal infections and specific PSE services. Using the Ebsco database, we identified case-control studies, cross-sectional surveys, case reports, and review studies investigating this relationship. Bacterial infections, particularly, mycobacterium infections are commonly reported, while viral risks are less well characterized. No information was found on fungal infection risks. Very limited evidence is available for some services, including manicures, hair styling, and barbering. Studies related to pedicures, although few, do establish a clear link between mycobacterium infections of the lower legs and the use of re-circulating footbaths. Waxing has been implicated in bacterial infection outbreaks due to poor infection control practices. The majority of studies identified are case reports, which provide limited information on the specifics of the services, and do not allow for an assessment of the PSE-related burden of illness. Some studies, however, do point to specific risk factors, routes of transmission, and research needs that can help to better inform PSE-related policy and practice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.008 | 0.001 |
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".