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Record W4285085521 · doi:10.5539/hes.v12n3p72

Model of Development of Learning Activities to Promote Mental Health among the Older Adults at Senior Citizens School

2022· article· en· W4285085521 on OpenAlexvenueno aff
Phetcharee Rupavijetra, Prachyanun Nilsook, Pornpun Manasatchakun, Sakchai Chaiyarak, Chetthapoom Wannapaisan

Bibliographic record

VenueHigher Education Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersKing Mongkut's University of Technology North BangkokChiang Mai UniversitySuan Dusit University
KeywordsPsychologyMental healthQualitative researchPaintingGerontologyMedical educationPedagogyMedicineVisual artsSociology

Abstract

fetched live from OpenAlex

This research aimed to develop learning activities in drawing and painting to promote mental health among older adults and to study the results and the satisfaction of the older adults towards learning activities in drawing and painting. The qualitative and quantitative research methods were conducted. The samples consisted of 200 older people, who were members of the senior citizens school in the community, as well as 12 school administrators and related staff who were involved with the activities designed. Quantitative data were analyzed using frequency and percentage, and qualitative data were analyzed by using content analysis. The findings revealed that the process of designing learning activities included three main activities: 1) preparing before learning through hand massage; 2) drawing by using exercise sheets and 3) painting or coloring on designed sheets. The observation results showed most of the older adults showed confidence in drawing and painting, and most of them had fun during drawing and painting activities. The older adults were satisfied with the overall program at the highest level (97%). The experience of learning from the activities indicated that they were happy with the designed learning activities. The outcomes of the designed learning activities support the older adults in terms of potential, value awareness, and self-efficacy.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.040
GPT teacher head0.377
Teacher spread0.337 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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