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Record W4402994298 · doi:10.56397/sps.2024.09.04

The Mediating Role of Emotional Stability and Social Skills in Psychological Resilience Following Emotional Trauma

2024· article· en· W4402994298 on OpenAlexaff
L. B. Thaddeus

Bibliographic record

VenueStudies in Psychological Science · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologySocial emotional learningResilience (materials science)Psychological resilienceEmotional traumaEmotional exhaustionDevelopmental psychologyPsychotherapistClinical psychologyBurnout

Abstract

fetched live from OpenAlex

Emotional trauma can have a profound impact on an individual’s psychological well-being, leading to various adverse outcomes such as anxiety, depression, and post-traumatic stress disorder. However, not all individuals respond to trauma in the same way, with some exhibiting remarkable resilience. This paper explores the mediating roles of emotional stability and social skills in fostering psychological resilience following emotional trauma. Emotional stability enables individuals to regulate their emotions, maintain composure, and utilize adaptive coping strategies in the face of adversity. Social skills facilitate the development of supportive relationships that provide essential emotional and practical assistance. Together, these attributes create a synergistic effect that enhances resilience, allowing individuals to navigate challenges and recover from emotional trauma effectively. By examining the interplay between emotional stability and social skills, this paper underscores the importance of integrated interventions aimed at strengthening these traits to promote psychological resilience. The findings suggest that cultivating emotional stability and social skills can significantly improve individuals’ ability to cope with trauma and lead fulfilling lives despite adversity.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.070
GPT teacher head0.502
Teacher spread0.433 · 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.

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
Published2024
Admission routes1
Has abstractyes

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