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Record W4381052411 · doi:10.1504/ijhd.2023.131530

Life satisfaction and Maslow's hierarchy of needs: an international analysis of the world values survey

2023· article· en· W4381052411 on OpenAlexaff
Hailey Pawsey, Kenneth M. Cramer, Denise Deblock

Bibliographic record

VenueInternational Journal of Happiness and Development · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaslow's hierarchy of needsWorld Values SurveyHierarchyLife satisfactionPsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

To assess Maslow's hierarchy of needs ability to predict life satisfaction, the present study utilised Wave-7 (2017-2020) of the World Values Survey. Data included measures for each level in Maslow's hierarchy for 69,000 individuals across 48 nations. Maslow's hierarchy of needs hypothesised greater life satisfaction would be identified among respondents with greater health satisfaction at the first level; financial satisfaction and income at the second level; importance of family, friends, and love and belonging at the third level; and importance of work, leisure, and self-esteem at the fourth level. Analyses at both respondent and national levels were supportive. Among respondents, life satisfaction was predicted by financial satisfaction, health, family, friends, work, and leisure; among nations, only financial satisfaction was significant, regardless of wealth distribution. These findings offer important insight into Maslow's hierarchy and life satisfaction, highlighting financial satisfaction as a salient factor in the enjoyment of a satisfying life.

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)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.360
Teacher spread0.306 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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