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Record W4297998714 · doi:10.1037/pas0001167

Examining the within- and between-person structure of a short form of the positive and negative affect schedule: A multilevel and dynamic approach.

2022· article· en· W4297998714 on OpenAlexafffund
Eric M. Cooke, Noémi Katalin Schuurman, Yao Zheng

Bibliographic record

VenuePsychological Assessment · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of Alberta
KeywordsPsychologyAffect (linguistics)PsycINFOMultilevel modelStructural equation modelingDevelopmental psychologyClinical psychologyMEDLINE

Abstract

fetched live from OpenAlex

= 272) measured daily consecutively over 1 month. Additionally, dynamic relations between positive and negative affect, depressive symptoms, stress, and physical symptoms were examined. Results support a three factors within and two factors between multilevel structural model. Distinct dynamic relations were observed among positive affect, negative affect, stress, and physical symptoms at the within level. Positive and negative affect were correlated with depressive symptoms, stress, and physical symptoms at the between level. Findings indicate the need to disentangle structural components of positive and negative affect when conducting intensive longitudinal studies to examine correlates linked to dynamic affective processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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)0.001
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 score0.274
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.129
GPT teacher head0.448
Teacher spread0.319 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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