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Record W2028496166 · doi:10.1080/17439760.2010.516763

The benefits of self-compassion and optimism exercises for individuals vulnerable to depression

2010· article· en· W2028496166 on OpenAlexaff
Leah B. Shapira, Myriam Mongrain

Bibliographic record

VenueThe Journal of Positive Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyOptimismPsychological interventionSelf-criticismSelf-compassionMoodClinical psychologyHappinessDepression (economics)PersonalityIntervention (counseling)Social psychologyMindfulnessPsychiatry

Abstract

fetched live from OpenAlex

The effectiveness of two online exercises intended to help individuals experience (1) self-compassion (n = 63) and (2) optimism (n = 55) were compared to a control intervention where participants wrote about an early memory (n = 70). A battery of tests was completed at 1 week following the exercise period, and at 1-, 3-, and 6-month follow-ups. Both active interventions resulted in significant increases in happiness observable at 6 months and significant decreases in depression sustained up to 3 months. The interventions were examined in relationship to dependency and self-criticism, both related to vulnerability to depression. Individuals high in self-criticism became happier at 1 week and at 1 month in the optimism condition in the repeated measures analysis. A sensitivity test using multi-level modeling failed to replicate this effect. More mature levels of dependence (connectedness) were related to improvements in mood up to 6 months in the self-compassion condition. This study suggests that different personality orientations may show greater gains from particular types of positive psychology interventions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.359
Teacher spread0.337 · 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 designNon-randomized trial
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

Citations380
Published2010
Admission routes1
Has abstractyes

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