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Record W4411069021 · doi:10.55606/jikki.v5i2.6042

Hubungan Self Efficacy dengan Quarter Life Crisis pada Mahasiswa Tingkat Akhir di Universitas Negeri Padang

2025· article· en· W4411069021 on OpenAlexaboutno aff
Juwita Putri Andini, Yolivia Irna Aviani

Bibliographic record

VenueJurnal Ilmu Kedokteran dan Kesehatan Indonesia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Sociology, Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PsychologyHumanitiesPolitical scienceArtHistory

Abstract

fetched live from OpenAlex

With a high level of self-efficacy, students can face quarter-life crisis easily. People who have self-efficacy will focus and do their tasks without any problems in difficult situations. Students who have self-efficacy are more confident and able to reach their full potential. In addition, it helps students acquire the skills needed for future success. The purpose of this study was to empirically investigate the relationship between self-efficacy of final year students at Padang State University and quarter-life crisis. This study combines cluster sampling strategy with quantitative approach. A total of 292 final year students at Padang State University became the research sample. With an error rate of 5%, this sample was taken using Isaac and Michael table. The data analysis method used is Pearson Product Moment. The results showed that quarter-life crisis and self-efficacy of final year students at Padang State University were each in the moderate range with a proportion of self-efficacy of 39.7% and quarter-life crisis of 28.4%. The research hypothesis stating that there is a significant negative relationship between self-efficacy and quarter life crisis in final year students at Padang State University is accepted based on the hypothesis test which produces a value of r = -0.469 with a significance level of p = 0.000 (p < 0.05).

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.351
Teacher spread0.317 · 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 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

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