HOW TO TREAT AND WHAT TO DO AT HOME?
Bibliographic record
Abstract
• Use a recommended headlice product. Always speak to a pharmacist about the product that is appropriate for you and your family. Follow directions specifi c to the product being used. • Head lice products kill the head lice and many eggs. **Remove nits with a nit comb or drag the nit down the hair shaft with your fi nger nails. Use a bright light so you can properly see the nits. • Check for head lice daily, and remove any nits that are present. It is important that you take the time to do this. It is recommended to remove all nits as head lice products are not 100 % eff ective. • One treatment may not be enough, and a second treatment may be required 7- 10 days later. Overuse of products can be hazardous. • In order for treatment to be fully eff ective, you must properly clean your house. This includes: • Wash all clothing and personal items (including hats, scarves, hair brushes, combs, towels, bedding, stuff ed animals, etc.) in hot soapy water. • Vacuum surfaces that have contact with heads, for example: sofas, seats of cars. • Items that cannot be washed should be drycleaned or placed in an airtight plastic bag for two weeks. • Clean frequently at the same time as treatments. REMINDER You should only use head lice treatment products if head lice are present. There is no evidence that products such as tea tree oil or aromatherapy work to treat head lice. At this time there are NO treatments available to prevent head lice. FOR MORE INFORMATION • Visit the Canadian Pediatric Society website at www.cps.ca • Contact your local school • Contact your local Public Health Nursing offi ce
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.190 | 0.144 |
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".