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Record W4206980958 · doi:10.1177/00221678211072450

Existential Meaninglessness Scale: Scale Development and Psychometric Properties

2022· article· en· W4206980958 on OpenAlexaff
P. F. Jonah Li, Y. Joel Wong, Keiko M. McCullough, Ling Jin, Chiachih D. C. Wang

Bibliographic record

VenueJournal of Humanistic Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExistentialismPsychologyDeath anxietyConfirmatory factor analysisConstruct validityScale (ratio)Exploratory factor analysisInternal consistencySocial psychologyClinical psychologyAnxietyPsychometricsStructural equation modelingEpistemologyStatisticsPsychiatry

Abstract

fetched live from OpenAlex

Grounded in a tripartite existential meaninglessness model, the authors developed the 18-item Existential Meaninglessness Scale (EMS) to assess one’s concern and anxiety of existential meaninglessness. Across three samples, the EMS’s factor structure and evidence of convergent, criterion-related, and incremental validity, internal consistency, and test–retest reliability were examined. Exploratory factor analyses demonstrated three dimensions of the EMS: incomprehension, purposelessness, and insignificance. Confirmatory factor analyses revealed that a bifactor model was a better fit to the data than other models. The bifactor model provided evidence for a general factor and measurement invariance. Ancillary bifactor indices indicated EMS’s unidimensionality. Findings of bivariate correlations and hierarchical regression analyses provided evidence for different aspects of construct validity and internal consistency. Both the Concern and Anxiety measures of the EMS positively predicted depressive symptoms and suicide ideation above and beyond the effects of general existential meaninglessness, general feelings of anxiety, and presence of meaning in life. Based on the findings, the authors discuss future research directions on existential meaninglessness using the EMS.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.311
Teacher spread0.264 · 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.

Study designNot applicable
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

Citations19
Published2022
Admission routes1
Has abstractyes

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