MétaCan
Menu
Back to cohort
Record W4413952258 · doi:10.61987/jemr.v4i2.957

Exploring the Quarter Life Crisis: Management Dimensions and Factors Influencing Early Adulthood Transitions Across Cultures and Educational Backgrounds

2025· article· en· W4413952258 on OpenAlexaboutno aff
Dhita Fadhillah Azza, Muhammad Krisnanda Candra Mahkota, Gilar Bagus Permana, Lucia Rini Sugiarti, Fendy Suhariadi

Bibliographic record

VenueJournal of Educational Management Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PsychologyGeography

Abstract

fetched live from OpenAlex

This study aims to identify the factors that influence the quarter-life crisis in early adulthood. A qualitative method was employed to reveal and understand this phenomenon within its unique context as experienced by individuals. Data were collected through observation and interviews and were analyzed using coding techniques. The findings indicate that anxiety about the future, pressure from family, fear of disappointing parents, and low self-confidence are key factors. Individuals also tend to compare themselves with others, particularly those deemed more successful. Various emotional responses, including frustration, hopelessness, and disappointment, were identified. The study also highlights the role of religious beliefs in coping with the crisis, although not all participants found this approach effective. Support from family and close friends is considered essential in overcoming the crisis, along with accepting one’s life process and engaging in self-exploration. Educational institutions can support early adults by offering mentorship, career guidance, and stress management programs, helping students navigate the quarter-life crisis and transition smoothly into adulthood while promoting emotional well-being and resilience.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0000.003
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 head0.278
GPT teacher head0.497
Teacher spread0.219 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational Management ResearchSame topicRetirement, Disability, and EmploymentFrench-language works237,207