An Observational Study on Barbers’ Practices and Associated Health Hazard in Fiji
Bibliographic record
Abstract
BACKGROUND: The barbers’ work is associated to many infectious diseases which lead to major cause of morbidity and mortality in human’s population globally. This study aimed to determine barbers’ practices and health hazards associated with their profession in Fiji. MATERIALS & METHODS: A cross sectional quantitative study was conducted to collect data from 50 observational sessions among barbers in Suva, Fiji in 2020. A sample of 25 barbers were randomly selected to participate in this study. A checklist was used to record and collect data. Out of the 25 barbershops observed none of them carried out any form of sterilization. RESULTS: Only 4 (16%) did some form of disinfection with home bleach and savalon randomly while 84% did not have any form of decontamination in place. The results further illustrate that 22 (88%) of the disinfection were not potent while 3 (12%) were unknown. Similarly, 22 (88%) had inappropriate methods of disinfection and 3 (12%) were questionable. None of the barbershops observed had supply of hot water and only 15 (60%) had sufficient privy and hand washing facilities. Furthermore, only 6 (24%) used PPEs compared to 19 (76%) were in non-compliance. CONCLUSION: This finding calls for immediate attention of authorities to enforce relevant laws and create awareness and training to improve standards in barbering profession.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".