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The effect of physical activity on depression in adolescence and emerging adulthood: A growth‐curve analysis<sup>☆</sup>

2015· article· en· W2048055392 on OpenAlexaff
Meghan L. McPhie, Jennine S. Rawana

Bibliographic record

VenueJournal of Adolescence · 2015
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsYork University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsDepression (economics)Latent growth modelingPsychologyGrowth curve (statistics)Physical activityLongitudinal studyDevelopmental psychologyYoung adultMental healthStructural equation modelingClinical psychologyGrowth modelPsychiatryMedicine

Abstract

fetched live from OpenAlex

This study examined the influence of physical activity on the trajectory of depression from adolescence through emerging adulthood (EA). Using data from the National Longitudinal Study of Adolescent Health (Waves I to IV), latent growth curve modeling was performed to assess how physical activity and gender influenced depression across adolescence and EA. Higher levels of physical activity in mid-adolescence were associated with lower levels of depression during mid-adolescence and slower inclines and declines in depression over time. Boys had lower levels of depression in mid-adolescence and slower inclines and declines in depression over time compared to girls. Findings provide evidence that current theories on understanding depression and mental health prevention programs may be enhanced by the inclusion of physical activity.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.020
GPT teacher head0.325
Teacher spread0.306 · 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

Citations100
Published2015
Admission routes1
Has abstractyes

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