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Record W4283591911 · doi:10.18006/2022.10(3).539.543

A pilot study of Resilience Programme through Group Dynamics on Academic Problems among the Matthayom Suksa 1 Students of Chiang Mai University Demonstration School

2022· article· en· W4283591911 on OpenAlexaboutno aff
Chanakarn Kumkun, Supat Chupradit, Pornpen Sirisatayawong

Bibliographic record

VenueJournal of Experimental Biology and Agricultural Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsChiang maiResilience (materials science)PsychologyPsychological resilienceMedical educationSession (web analytics)MedicineSociologySocial psychologyComputer scienceSocioeconomics

Abstract

fetched live from OpenAlex

The objective of this study was to develop a Resilience Programme through Group Dynamics on Academic Problems among Matthayom Suksa 1 Students at Chiang Mai University Demonstration School. For this, four junior high school students were selected as respondents. The effect of the resilience program was evaluated through a general questionnaire, the Canadian Occupational Performance Measures (COPM), and Resilience Inventory. Further, the resilience program was developed by using cognitive behavioral therapy combined with acceptance and commitment therapy, group dynamics, and resilience according to the concept of Grotberg. The total period of the program was 11 weeks, with 1 session per week lasting for 60 minutes. Results of the study revealed that all the selected respondents had higher academic performance and most of them (75%) had higher academic satisfaction and resilience score. After participating in this program, the samples had a higher average resilience score (114.5) as compared to those before participating in the program (107.5). The results of this study can be concluded that the newly developed resilience program can improve the resilience component in almost all the students. Hence, it can be practiced in junior high school students to manage their academic problems. This program can also be a prototype for developing future resilience programs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.244
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.371
Teacher spread0.332 · 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.

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
Published2022
Admission routes1
Has abstractyes

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