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Record W2007533235 · doi:10.1037/a0025401

The presence of a best friend buffers the effects of negative experiences.

2011· article· en· W2007533235 on OpenAlexaff
Ryan Adams, Jonathan B. Santo, William M. Bukowski

Bibliographic record

VenueDevelopmental Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsNegativity effectPsychologyDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

The goal of the current study was to examine how the presence of a best friend might serve as protection against the effect of negative experiences on global self-worth and the hypothalamic-pituitary-adrenocortical axis (HPA axis). A total of 103 English-speaking male (n = 55) and female (n = 48) participants from Grade 5 (M = 10.27 years) and Grade 6 (M = 11.30 years) completed booklets about their experiences that occurred 20 min previously and how they felt about themselves at the moment, and they provided saliva multiple times per day over the course of 4 consecutive days. Having a best friend present during an experience significantly buffered the effect of the negativity of the experience on cortisol and global self-worth. When a best friend was not present, there was a significant increase in cortisol and a significant decrease in global self-worth as the negativity of the experience increased. When a best friend was present, there was less change in cortisol and global self-worth due to the negativity of the experience.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.300
Teacher spread0.269 · 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

Citations185
Published2011
Admission routes1
Has abstractyes

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