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Record W2291746544 · doi:10.1080/00224545.2015.1135779

The social mentality theory of self-compassion and self-reassurance: The interactive effect of care-seeking and caregiving

2016· article· en· W2291746544 on OpenAlexafffund
Nicola Hermanto, David C. Zuroff

Bibliographic record

VenueThe Journal of Social Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntrapersonal communicationSelf-compassionPsychologySelf careInterpersonal communicationCompassionClinical psychologySocial psychologyMindfulnessHealth care

Abstract

fetched live from OpenAlex

The aim of this study was to test social mentality theory, which views self-compassion/reassurance as a form of intrapersonal relating in which the interpersonal mentalities of care-seeking and caregiving are activated. Self-report measures of motivations, cognitions, and behaviors related to seeking and receiving care from others were administered to 195 students. Self-compassion/reassurance was predicted by the interaction of care-seeking and caregiving, with the positive effect of care-seeking intensified at high caregiving. As hypothesized, the combination of high care-seeking and high caregiving predicted the highest level of self-compassion/reassurance. The lowest level of self-compassion/reassurance was predicted by the combination of low care-seeking and high caregiving consistent with the concept of compulsive caregiving. Findings suggest that fostering a kinder way of relating to oneself may be achieved through more effective care-seeking and caregiving with others.

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)0.006
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.364
Teacher spread0.351 · 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

Citations120
Published2016
Admission routes2
Has abstractyes

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