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Record W4220681679 · doi:10.1007/s12144-022-03038-6

Socioeconomic inequalities in adolescent health complaints: A multilevel latent class analysis in 45 countries

2022· article· en· W4220681679 on OpenAlexaff
Nour Hammami, Inese Gobiņa, Justė Lukoševičiūtė, Michaela Kostičová, Nelli Lyyra, Geneviève Gariépy, Kastytis Šmigelskas, Adriana Băban, Marta Malinowska-Cieślik, Frank J. Elgar

Bibliographic record

VenueCurrent Psychology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsMcGill UniversityPublic Health Agency of CanadaUniversity of Regina
Fundersnot available
KeywordsSocioeconomic statusMultilevel modelLatent class modelPsychologyPsychological interventionSocial classDemographyEnvironmental healthMedicinePsychiatryPopulation

Abstract

fetched live from OpenAlex

Abstract Our study evaluated the relationship between adolescent health complaints and socioeconomic position in 45 countries. Data are from the 2017/2018 international Health Behaviour in School-aged Children survey which used proportionate sampling among adolescents aged 11 to 15 years old (n=228,979). Multilevel, multinomial regression analysis assessed the association between the multilevel latent classes with socioeconomic status (SES; at the household and country level). Three distinct latent classes were identified: No Complaints, Psychological Complaints, and a Physical and Psychological Complaints class; where, low household SES was highest for the physical and psychological complaints class. The findings suggest that health promotion policies and interventions among adolescents should consider the specific needs of adolescents living with low household SES as they report more subjective health complaints.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.225
GPT teacher head0.536
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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