MétaCan
Menu
Back to cohort
Record W4401235793 · doi:10.55849/jnhl.v2i1.939

Coping Strategies In Overcoming Academic Stress Among High School Students

2024· article· en· W4401235793 on OpenAlexaff
Fitriah Handayani, Bouyea Jonathan, Wang Joshuar, Eladdadi Mark, Chandra Halim

Bibliographic record

VenueJournal Neosantara Hybrid Learning · 2024
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoping (psychology)PsychologyMathematics educationClinical psychology

Abstract

fetched live from OpenAlex

Academic stress is a problem often faced by high school students, it can affect their mental well-being and academic performance. Coping strategies play an important role in helping students deal with such stress. This study aims to explore various coping strategies used by high school students in dealing with academic stress and its impact on their mental well-being. This research uses a qualitative approach. Data were analyzed using the thematic analysis method to identify patterns of coping strategies used. The results of this study indicate that high school students use various coping strategies to deal with academic stress, such as social support from peers, exercise, and relaxation techniques such as meditation. Students also use problem-solving and cognitive restructuring strategies to change their perceptions of academic stressor situations. The conclusion of this research is that the coping strategies used by high school students play a very important role in helping them overcome academic stress and improve mental well-being. The importance of providing approaches in managing academic stress among students to promote their mental health. This research provides valuable insights for educators and counselors to develop appropriate interventions to support students to deal with academic stress more effectively.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.005
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 head0.019
GPT teacher head0.368
Teacher spread0.348 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal Neosantara Hybrid LearningSame topicHealth and Well-being StudiesFrench-language works237,207