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Record W3164859041 · doi:10.1017/s0954579421000626

Trajectories of depression and anxiety symptoms over time in the transition to university: Their co-occurrence and the role of self-critical perfectionism

2021· article· en· W3164859041 on OpenAlexaff
Shelby L. Levine, Nassim Tabri, Marina Milyavskaya

Bibliographic record

VenueDevelopment and Psychopathology · 2021
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsCarleton UniversityMcGill University
Fundersnot available
KeywordsPsychologyAnxietyPerfectionism (psychology)Depression (economics)Mental healthClinical psychologyDepressive symptomsTrait anxietyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Little is known about how mental health symptoms develop during the transition to university. Most anxiety and depression research fails to consider how symptom development differs over time across different individuals, and how symptom co-occurrence influences the severity of mental health problems. Students ( N = 658) completed online surveys on mental health prior to starting university and every 2 months until April. To better understand mental health problems during this transitional period, latent class growth curve analyses were run to determine how anxiety and depressive symptoms co-develop over time, as well, if self-critical perfectionism was a transdiagnostic risk factor for more severe symptom trajectories in this transition. About 40% of students experienced depression and anxiety symptoms prior to entering/during the transition to university. There is substantial variation between students in terms of how they experience depression and anxiety symptoms, and research needs to take this heterogeneity into account to properly identify which students might benefit most from resources. Self-critical perfectionism was a transdiagnostic risk factor, such that students higher in this trait experienced more severe anxiety and depressive symptom trajectories during this transition. This research further implicates the importance of understanding and studying individual differences in symptom development.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.417
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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 teacher head, 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

Citations22
Published2021
Admission routes1
Has abstractyes

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