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Research on Stress, Emotions and Mental Health

2023· article· en· W4387939513 on OpenAlexaff
Yinan Fan

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMental healthPsychologyStress (linguistics)Depression (economics)Applied psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Younger generations in this fast-moving world have more commitments than their abilities, so they tend to carry burden which they cannot bear. At the same time, rate of suicide is on the rise, especially among youngsters aged between 15 – 19 years old. Relevant research reports have concluded that mental disorders and depression are the important caused of suicide. This research investigates the impact of the stress on mental behavior and emotions in the present world through analyses of stress management, work plan, self-satisfaction, virtual world and emotional impacts. It is concluded that 80 percent participants engaged with have experienced stress at one time in their life, while 14 percent propose extremely stressed. Only 6 percent knew how to deal with their situation and find solution. Therefore, in this situation with social pressure, there is a great need for information from mental health practitioners to provide information on stress relieving to help the young people affected by psychological challenges due to stress

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.138
GPT teacher head0.533
Teacher spread0.395 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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