Health and Safety in Nail Salons: A Cross-Sectional Survey
Bibliographic record
Abstract
OBJECTIVES: In recent years, nail salons have become more abundant than ever. The majority are small businesses, often employing immigrant women. Nail technicians have many exposures at work including chemical, biological, physical, and ergonomic hazards but few data exist on their symptoms or occupational health and safety practices, particularly in the Canadian context. The aim of this study was to learn about nail technicians, their work, and their health and safety practices. METHODS: Nail technicians were recruited from nail salons in Toronto, Ontario. Participants completed an anonymous survey with questions about demographics, work tasks, workplace health and safety practices, and symptoms (skin, respiratory, and musculoskeletal). RESULTS: A total of 155 nail technicians (95% female) participated in the survey. The majority of nail technicians performed manicures (99%) and pedicures (96%) and applied shellac polishes (86%). Only a third (34%) applied acrylic artificial nails. The reported use of personal protective equipment (PPE) was very high; 88% reported using a mask at work and 96% reporting using gloves. The most common symptoms reported by technicians were neck (44%) and back pain (38%). Skin and respiratory symptoms were less common with 6% of technicians reporting cough, 8% wheeze, and 5% a current rash. Technicians working over 30 h per week reported more neck pain (52 vs. 32%, P = 0.02). Technicians who reported using shellac polishes were more likely to report a runny nose (25 vs. 0%, P = 0.01). CONCLUSIONS: Nail technicians in Toronto, Canada are experiencing work-related symptoms. Musculoskeletal symptoms were the most common symptoms reported. Much of the focus on nail salons and health has been on chemical exposures, but ergonomic hazards should not be overlooked. Efforts to increase knowledge and improve occupational health in nail salons should include information on multiple possible workplace hazards and how to reduce impacts of exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".