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Record W4414302157 · doi:10.5539/jedp.v15n2p15

The Development of a Training Program to Enhance Mental Immunity in the New Normal Era for Roi Et Rajabhat University Students Based on the Concept of Psychology. (Thailand)

2025· article· en· W4414302157 on OpenAlexvenueno aff
Prasarn Sripongplerd

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsSample (material)Cluster samplingScale (ratio)Test (biology)Sampling (signal processing)Mental healthTraining (meteorology)

Abstract

fetched live from OpenAlex

The main purpose of this research were to 1) develop the training program to enhance mental immunity in the new normal era for university students based on the concept of psychology. 2) to study the results of a training program to enhance mental immunity for university students based on the concept of psychology. The sample consisted of 28 students from Roi Et Rajabhat University, selected by cluster random sampling. Multi-stage sampling was employed. The research instrument includes 1) a training program to enhance mental immunity in the new normal era based on the concept of psychology and 2) a measurement of mental immunity. It was a 5-rating scale form with 25 items, each with an item discrimination of 0.21 - 0.67 and a reliability of 0.82. Statistics employed for analyses of the data included percentage, mean, standard deviation, and t-test for dependent samples. The results of the research were as follows: 1) The training program to enhance mental immunity consisted of twelve activities and was at “the high” level of property (M = 4.20, S.D. = 0.70). 2) The sample who participated in the program had a level of mental immunity after the experiment higher than before the experiment, statistically significant at the .05 level.

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 score0.003
Threshold uncertainty score0.011

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.069
GPT teacher head0.479
Teacher spread0.410 · 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
Published2025
Admission routes1
Has abstractyes

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