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Record W4411161716 · doi:10.1111/jcpp.14175

Dynamics of depression symptoms in adolescents during three types of psychotherapy and post‐treatment follow‐up

2025· article· en· W4411161716 on OpenAlexafffund
Madison Aitken, Sharon Neufeld, Clement Ma, Ian Goodyer

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsPublic Health OntarioYork UniversityUniversity of TorontoCentre for Addiction and Mental Health
FundersHealth Technology Assessment ProgrammeCundill Centre for Child and Youth DepressionNational Institute for Health and Care ResearchCentre for Addiction and Mental HealthDepartment of Health and Social CareNIHR Cambridge Biomedical Research CentreWellcome Trust
KeywordsPsychopathologyPsychologyPsychosocialPsychological interventionDepression (economics)PopulationClinical psychologyRandomized controlled trialPsychiatryCognitive behavioral therapyCentralityCognitionMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: According to the network theory of mental disorders, psychopathology emerges from symptoms that causally influence one another and create interconnections and feedback loops that maintain atypical mental states. Analysis of symptom networks during and following psychotherapy may provide clues to some of the mechanisms through which change occurs. Youth with depression are an important population in which to better understand psychotherapy mechanisms because current evidence-based interventions for this population show only modest effects. METHODS: Participants were adolescents with major depressive disorder (N = 465; ages 11-17; 75% female) in a randomized controlled trial comparing cognitive behavioral therapy, short-term psychoanalytical psychotherapy, and brief psychosocial intervention (IMPACT, ISRCTN83033550). Eleven self-reported depression symptoms were used to compute two longitudinal networks: (1) treatment phase, using baseline, 6 and 12 weeks data; and (2) follow-up phase, using 36, 52, and 86 weeks data. RESULTS: During the treatment phase, all depression symptoms were interconnected. Symptoms of insomnia and fatigue showed the highest outstrength centrality (ability to predict other symptoms over time). In contrast, few symptoms were interconnected during the post-treatment phase except worthlessness, which had the highest outstrength centrality. Allowing network parameters to differ across the three treatment types improved model fit during the treatment phase and revealed that symptoms with the highest outstrength centrality varied by treatment type. CONCLUSIONS: Individual symptoms may make key contributions to subsequent depressive psychopathology in adolescents. Longitudinal network analysis reveals that insomnia and fatigue predict other symptoms, allowing for consideration of specific mechanisms associated with depression treatment. The findings further suggest that negative cognitions about the self may emerge as a central putative cognitive vulnerability in those with a history of depression. Our exploratory findings also suggest that the three therapies (cognitive behavioral therapy, short-term psychoanalytical psychotherapy, and brief psychosocial intervention) may have achieved equifinality in part through different mechanisms.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.373
Teacher spread0.360 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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