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Record W4409407405 · doi:10.30762/welfare.v2i2.1447

Mengatasi Quarter Life Crisis dan Meningkatkan Potensi Diri Melalui Metode Self-Healing

2024· article· en· W4409407405 on OpenAlexaboutno aff
Reza Hardian Pratama, Mahendra Pratama, Anita Anita, Harold Kevin Afredo, Rizki Agung Wibowo

Bibliographic record

VenueWelfare Jurnal Pengabdian Masyarakat · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PsychologyHistory

Abstract

fetched live from OpenAlex

The Quarter Life Crisis (QLC) is a phenomenon often experienced by teenagers in their early 20s. It can cause anxiety, confusion, and a sense of loss of direction in life. Therefore, it is important to equip adolescents with knowledge and skills that can help them deal with QLC and increase their potential. The purpose of this community service is to introduce QLC to 11th grade students of SMAN 13 Bandar Lampung and equip them with self-healing methods to increase their potential. The methods used in this activity were lectures, discussions, and self-healing practices. The results showed that after participating in this activity, students had a better understanding of QLC and could identify its signs. In addition, students were also able to apply self-healing methods to increase their potential. In conclusion, this community service activity provided significant benefits for 11th grade students at SMAN 13 Bandar Lampung. Students become more prepared to face QLC and have higher self-confidence to develop their potential.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.003

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 head0.054
GPT teacher head0.401
Teacher spread0.347 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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