Bibliographic record
Abstract
BACKGROUND: Head lice are a source of amusement for outsiders and an embarrassing nuisance to those who have to deal with them. Our study collected the emotions experienced by people dealing with head lice. An area with extremely sparse literature, our purpose is to inform the development of more effective programs to control head lice. METHODS: We asked "what were your feelings upon discovery of head lice?" as part of a study exploring the experience of those treating head lice. A short questionnaire was available via the authors' head lice information internet site. A total of 294 eligible responses were collected over several months and analyzed, supported by QSR N6. RESULTS: The predominantly female (90 · 9%) respondents were residents of Australia (56 · 1%), USA (20 · 4%), Canada (7 · 2%), or UK (4 · 4%), and working full-time (43·0%) or part-time (34 · 2%). Reactions and feelings fell into three categories: strong (n = 320; 79% of all stated emotions), mediocre (n = 56; 20%), and neutral (n = 29; 9 · 8%). There were no positive emotions. COMMENT: The significant negative reaction was expected. The range of feeling expressed demonstrates the stigma held for these ectoparasites within western market economies. This contrasts with conceptions of head lice in traditional societies. The negative social effects of this perception create more problematic issues than the infection itself; these include quarantine, overtreatment, and a potentially negative psychological impact. Head lice control strategies and programs that address these negative emotional reactions may prove more effective than current biomedical focus.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".