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Record W4406698876 · doi:10.1080/07420528.2025.2453236

Morningness-eveningness and mental health: Initial evidence of the moderating roles of mattering and anti-mattering

2025· article· en· W4406698876 on OpenAlexaff
Joanna Gorgol-Waleriańczyk, Wojciech Waleriańczyk, Gordon L. Flett

Bibliographic record

VenueChronobiology International · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsYork University
Fundersnot available
KeywordsChronotypePsychologyMental healthDevelopmental psychologyClinical psychologyCircadian rhythmPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

Mental health problems are more prevalent in evening-oriented individuals than in their morning-oriented counterparts. Recently, research has offered first insights into how the negative effects of eveningness on mental health and well-being can be magnified or alleviated depending on accompanying psychological characteristics. In the current study, we evaluated how eveningness relates to mattering and anti-mattering and whether mattering and anti-mattering can moderate the association between eveningness and mental health. The participants were 692 Polish adults (337 women, 355 men) aged between 21 and 57 years (M ± SD: 39.76 ± 9.63). All participants completed measures of morningness-eveningness and depressive and anxiety symptoms, the General Mattering Scale (GMS) and the Anti-Mattering Scale (AMS). Conducted analyses showed that 1) the Polish versions of GMS and AMS have appropriate reliability and validity, 2) eveningness is negatively associated with mattering and positively associated with anti-mattering, depressive, and anxiety symptoms, and 3) the magnitude of the association between eveningness and mental health symptoms increased with higher anti-mattering and lower mattering. Overall, this study presents the first evidence of how feelings of being important and being valued may buffer against the negative effects of eveningness on mental health.

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 score0.118
Threshold uncertainty score0.270

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 head0.019
GPT teacher head0.347
Teacher spread0.328 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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