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Record W1965949005 · doi:10.1080/17439760.2011.577089

Are positive psychology exercises helpful for people with depressive personality styles?

2011· article· en· W1965949005 on OpenAlexaff
Susan Sergeant, Myriam Mongrain

Bibliographic record

VenueThe Journal of Positive Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsYork University
Fundersnot available
KeywordsGratitudePsychologyHappinessWell-beingIntervention (counseling)Clinical psychologyPositive psychologySelf-esteemPersonalityDepression (economics)Developmental psychologySocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Two exercises involving gratitude and uplifting music were tested for their ability to improve well-being in self-critical and needy individuals. In this study, 772 adults completed measures of depressive symptoms, physical symptoms, happiness, and self-esteem and then practiced the gratitude, music, or control exercise (recalling early childhood memories) for 1 week. Follow-up measures were administered after the intervention period, and 1, 3, and 6 months later. Participants in both the gratitude and the music condition reported greater increases in happiness over time than participants in the control condition. Self-critics were particularly responsive to the gratitude intervention, whereas needy individuals found the exercises ineffective and even detrimental to their self-esteem. These results highlight the importance of identifying individual differences in response to positive psychology exercises.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.000

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 head0.057
GPT teacher head0.361
Teacher spread0.304 · 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

Citations95
Published2011
Admission routes1
Has abstractyes

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