Head louse infestations: the “no nit” policy and its consequences
Bibliographic record
Abstract
Health authorities in the USA, Canada and Australia recommend a "no nit" policy, i.e. the immediate dismissal of all children who have head lice, eggs and/or nits on their hair from school, camp or child-care settings. These children would be readmitted to the institution only when all head lice, eggs and nits have been removed. The "no nit" policy assumes that all nits seen when examining the scalp are viable and therefore the infested individual should be treated for lice, and all nits must be removed from the scalp. However, it has been repeatedly shown that only a small number of children who have nits on their scalp are also infested with living lice. Accordingly, in the USA alone 4-8 million children are treated unnecessarily for head lice annually, which amounts to 64% of all lice treatments. In addition, 12-24 million school days are lost annually. The annual economic loss owing to missed workdays by parents who have to stay home with their children adds US$4-8 billion to the country's economy. The policy also results in serious psychological problems for children and their parents. Therefore, the "no nit" policy should be abandoned and alternative ways of examination and treatment for head lice should be found.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".