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Record W4411535898 · doi:10.61994/cpbs.v3i.141

Efektivitas Shalat Tahajud terhadap Penurunan Tingkat Kecemasan Mahasiswa Rantau

2024· article· en· W4411535898 on OpenAlexaff
Nurdiana Nurdiana, Nida Shabirah, Dinta Rizka Irfianti, Nyayu Istiqomah

Bibliographic record

VenueProceeding Conference On Psychology and Behavioral Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Character Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrayerAnxietyPsychologyClinical psychologyData collectionVariety (cybernetics)Descriptive researchResearch methodPsychiatrySocial scienceSociology

Abstract

fetched live from OpenAlex

Stress and anxiety are problems that often occur in all circles regardless of age, profession or social background. It is based on a variety of experiences that could be a source of problems in life, thus triggering anxiety that disturbs, and affects a person's emotional well-being. One way to overcome these problems is to perform tahajud prayer. The purpose of this study was to determine the effectiveness of tahajud prayer on the anxiety level of regional students. The method used in this research is a type of descriptive qualitative research with data collection system in this study, through observation, literature study, and interviews. The sample in this study amounted to 3 students. The results and discussion of this study indicate that tahajud prayer can be one of the therapies that can help students become calmer and certainly reduce their anxiety levels. The conclusion of this study is that tahajud prayer provides positive effectiveness in reducing the level of anxiety experienced by regional student.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.003

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 head0.223
GPT teacher head0.486
Teacher spread0.262 · 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
Published2024
Admission routes1
Has abstractyes

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