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Record W4403348842 · doi:10.1007/s10964-024-02095-3

Generational Shifts in Adolescent Mental Health: A Longitudinal Time-Lag Study

2024· article· en· W4403348842 on OpenAlexafffund
Meghan E. Borg, Taylor Heffer, Teena Willoughby

Bibliographic record

VenueJournal of Youth and Adolescence · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsOntario Tech UniversityBrock University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsMental healthHealth psychologyPsychologyLongitudinal studyAnxietyLegal psychologyClinical psychologySample (material)Depression (economics)Public healthDevelopmental psychologyPsychiatryDemographyMedicine

Abstract

fetched live from OpenAlex

There is concern that adolescents today are experiencing a "mental health crisis" compared to previous generations. Research has lacked a longitudinal time-lag design to directly compare depressive symptoms and social anxiety of adolescents in two generations. The current study surveyed 1081 adolescents in the current generation (Mage = 14.60, SD = 0.31, 49% female) and 1211 adolescents in a previous generation (Mage = 14.40, SD = 0.51, 51% female) across the high school years (grades 9-12), 20 years apart. Mixed-effects analysis revealed that the Current-Sample reported higher and increasing mental health problems over time compared to the Past-Sample. Although most adolescents reported consistently low mental health problems, the Current-Sample had a higher proportion of adolescents who were consistently at risk across the high school years compared to the Past-Sample. These findings highlight while most adolescents in both generations do not report elevated mental health problems, there may be a small, yet growing, group of adolescents today at risk for experiencing a "mental health crisis".

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)0.002
metaresearch head score (Gemma)0.003
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.321
Teacher spread0.279 · 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
Published2024
Admission routes2
Has abstractyes

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