Bruk av reseptfrie smertestillende medikamenter blant multietnisk oslo-ungdom
Bibliographic record
Abstract
Background: Data on the use of over-the-counter (OTC) analgesics comparing different adolescent ethnic groups is scarce. In Oslo, almost one quarter of the adolescent population come from an immigrant background.\n\t\nObjective: The aim of this study is to describe possible differences in the use of OTC analgesics between Norwegian adolescents (15-16 year olds) and adolescents with Muslim and Other immigrant background. \n\nMethods: A survey on health and medication use was undertaken among 10 graders (15-16 year olds) in all Secondary Schools in Oslo, Norway. This was the youth part of the Oslo Health Study in 2000-2001. There were 7343 participants in the survey, 3612 boys and 3695 girls. \n\nResults: Norwegian adolescents were more likely to report use of OTC analgesics during the past four weeks, 24,4% of the boys and 57,5% of the girls reported using such medication. In the Muslim group, 17,9% of the boys and 34,5% of the girls reported use of OTC analgesics. Similar figures for Other immigrant background were 21,8% and 32,9%. Use within the Muslim group depended on nationality, among Pakistani girls only 24,0% had used these preparations, while 60,4% of Iranian girls reported the same. The differences were not as striking for boys.\n\n\nConclusions: A difference exists across ethnicity in adolescents’ use of OTC analgesics. Adolescents from Muslim and Other immigrant background countries used less OTC analgesics than ethnic Norwegians. The use within the Muslim group further depends on nationality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".