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Record W4310053516 · doi:10.1177/21677026221104735

Examining Unique Associations of Social Anxiety and Depression on Behaviorally Assessed Affective Empathy

2022· article· en· W4310053516 on OpenAlexfundno aff
Talha Alvi, David Rosenfield, Cecile S. Sunahara, Zachary Wallmark, Junghee Lee, Benjamin A. Tabak

Bibliographic record

VenueClinical Psychological Science · 2022
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyEmpathyAnxietyAnhedoniaPsychopathologySocial anxietyValence (chemistry)Affect (linguistics)CognitionDevelopmental psychologyClinical psychologyDepression (economics)Association (psychology)PsychiatryPsychotherapistSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

A growing body of research suggests that cognitive empathy (i.e., understanding other people’s mental states) may be impaired in socially anxious and depressed individuals. However, few studies have examined whether affective empathy (i.e., sharing other people’s emotional states, referred to as “affect sharing”) may likewise be impaired in either form of psychopathology. In Study 1 ( n = 202), we examined the unique association between social anxiety (or depression) and affect sharing and the moderating role of anhedonia and stimuli valence above and beyond depression (or social anxiety). No main or interaction effects were found for social anxiety or depression in the prediction of affect sharing. In Study 2, we conducted a direct replication of Study 1 with a larger sample ( n = 324), which confirmed our findings from Study 1. Thus, the unique effects of social anxiety and depression may be more related to difficulties in cognitive, rather than affective, empathic processes.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.224
GPT teacher head0.505
Teacher spread0.281 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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