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Record W3152678720 · doi:10.1016/j.heliyon.2021.e06783

Neuroticism versus emotionality as mediators of the negative relationship between materialism and well-being

2021· article· en· W3152678720 on OpenAlexaff
David Watson

Bibliographic record

VenueHeliyon · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPsychologyNeuroticismEmotionalityMediationMaterialismDevelopmental psychologyExtraversion and introversionPersonalitySocial psychologyBig Five personality traits

Abstract

fetched live from OpenAlex

The purpose of the study was to investigate the relationship between, neuroticism, emotionality well-being and materialism. A series of mediation analyses were conducted with data obtained from a set of questionnaires completed by University students. The results indicated that neuroticism and emotionality were mediators in the well-being-materialism relationship. However, this relationship is dependent upon whether neuroticism or emotionality is measured as the three neuroticism measures utilized were significant mediators whereas the HEXACO emotionality scale was not. A facet-level analysis was conducted with the IPIP-NEO facets of volatility and withdrawal and with the HEXACO facets of sentimentality/dependence and withdrawal. In either case, withdrawal was a significant mediator in the materialism well-being relationship, whereas volatility or sentimentality/dependence was not. The results highlight the differences between neuroticism and HEXACO emotionality and add additional insight into the relationship between materialism and lower well-being. These findings suggest possible methods of decreasing materialistic tendencies and increasing subjective well-being.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.113
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.043
GPT teacher head0.335
Teacher spread0.291 · 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 teacher head, 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

Citations4
Published2021
Admission routes1
Has abstractyes

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