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Record W3037497307 · doi:10.1177/0956797620919673

Does Happiness Improve Health? Evidence From a Randomized Controlled Trial

2020· article· en· W3037497307 on OpenAlexaff
Kostadin Kushlev, Samantha J. Heintzelman, Lesley D. Lutes, Derrick Wirtz, Jacqueline M. Kanippayoor, Damian Leitner, Ed Diener

Bibliographic record

VenuePsychological Science · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHappinessPsychologyRandomized controlled trialIntervention (counseling)PopulationClinical psychologyPhysical healthGerontologyMedicinePsychiatryMental healthSocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

Happier people are healthier, but does becoming happier lead to better health? In the current study, we deployed a comprehensive, 3-month positive psychological intervention as an experimental tool to examine the effects of increasing subjective well-being on physical health in a nonclinical population. In a 6-month randomized controlled trial with 155 community adults, we found effects of treatment on self-reported physical health-the number of days in the previous month that participants felt healthy or sick, as assessed by questions from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System Questionnaire. In a subsample of 100 participants, we also found evidence that improvements in subjective well-being over the course of the program predicted subsequent decreases in the number of sick days. Combining experimental and longitudinal methodologies, this work provides some evidence for a causal effect of subjective well-being on self-reported physical health.

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)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.063
GPT teacher head0.407
Teacher spread0.344 · 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; both teacher heads agree on what is shown here.

Study designRandomized 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

Citations102
Published2020
Admission routes1
Has abstractyes

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