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Record W4402681817 · doi:10.3390/engproc2024074065

Role of Emotional Maturity and Social Support in Predicting Quarter-Life Crisis in Emerging Adulthood Using Multiple Linear Regression Analysis

2024· article· en· W4402681817 on OpenAlexaboutno aff
Muhamad Nanang Suprayogi, Wira Bagus Santoso

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Maturity (psychological)Regression analysisLinear regressionPsychologyRegressionSocial supportEconometricsComputer scienceDevelopmental psychologyMachine learningMathematicsSocial psychologyHistory

Abstract

fetched live from OpenAlex

This study aims to examine the role of emotional maturity and social support in predicting the level of quarter-life crisis in emerging adulthood. The employed research method was multiple linear regression analysis. The participants were individuals aged 18 to 29 years. Further, 122 participants were selected using convenience sampling. The data were collected using a questionnaire survey based on the Multidimensional Scale of Perceived Social Support to assess social support and the quarter-life crisis scale based on the theory by Robbins and Wilner. To assess emotional maturity, we used the emotional maturity scale based on the theory by Walgito. Emotional maturity and social support were important in predicting the level of quarter-life crisis in emerging adulthood. Higher levels of emotional maturity and social support were associated with lower levels of quarter-life crisis experiences in emerging adulthood.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.344
Teacher spread0.322 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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