Trends in multiple health complaints in Polish adolescents in light of data from 30 European countries and Canada (2002-2018).
Bibliographic record
Abstract
BACKGROUND: Adolescence is a sensitive period accompanied by rapid developmental changes that can result in health complaints. The aim of the study was to describe the trend of subjective health complaints (HBSC-SCL) of Polish adolescents compared to their peers from 30 other countries and to rank all countries based on a proposed standardised measure. MATERIAL AND METHODS: Data from the Health Behaviour in School-Aged Children (HBSC) study collected from 2002 to 2018 were used. The overall number of respondents from 30 countries in the combined sample from five quadrennial rounds was 773,356, including 49.2% boys and 50.8% girls. The HBSC-SCL is a non-clinical measure consisting of eight health complaints, usually analysed in two dimensions of psychological and somatic symptoms. Linear regression analysis was applied to assess the significance of trends of the total index and two subindices in the combined sample and individual countries. RESULTS: A significant increasing trend for the eight-item index appeared in Poland only in 13- and 15-year-olds, while only among 15-year-olds in the combined sample from 30 countries. Standardised country rank varied between -1.85 and 2.48 (worst). The countries that achieved extreme negative values (>=1) are France, Hungary, Italy, and Sweden, and the rank for Italy is considerably higher than for other countries. In Poland, the standardised rank for psychological symptoms exceeded the threshold of +1 in 2018. CONCLUSIONS: The HBSC-SCL index could be useful for monitoring change in adolescent mental health. The proposed method of ranking may allow a broader view of the differences and similarities between countries and help to identify those performing unfavourably against cross-country patterns.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".