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Record W2804940960 · doi:10.21831/jk.v0i0.20626

Model senam lansia untuk kebugaran jasmani dan fungsi otak

2018· article· id· W2804940960 on OpenAlexaboutno aff
Edo Fralian Putra, Suharjana Suharjana

Bibliographic record

VenueJurnal Keolahragaan · 2018
Typearticle
Languageid
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPhysical therapyHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk menghasilkan sebuah senam lansia untuk kebugaran jasmani dan fungsi otak. Metode penelitian ini mengacu pada penelitian Borg & Gall dengan tahapan: studi pendahuluan, analisis desain draf awal, validasi ahli, uji coba produk, evaluasi, dan revisi. Subjek uji coba skala kecil 15 orang, subjek uji coba penelitian skala besar 57 orang, dan subjek uji efektifitas produk 12 orang. Penilaian para ahli dan instruktur menggunakan kuisioner. Pengukuran kebugaran jasmani lansia menggunakan tes uji jalan 6 menit, sedangkan untuk mengukur fungsi kognitif lansia menggunakan tes The Montreal Cognitif Assesment Indonesia (MoCA–Ina). Hasil penelitian menunjukkan, model senam lansia valid, serta layak dan efektif untuk kebugaran jasmani dan fungsi otak. Hasil validasi ahli kesehatan lansia dan akademisi sekaligus praktisi kesehatan termasuk “sangat baik” dengan rata-rata 81,87%. Kualitas produk hasil uji coba skala kecil “sangat baik” dengan rata-rata 86,25. Uji coba skala besar juga termasuk “sangat baik” dengan rata-rata 98,12%. Hasil uji efektifitas produk menunjukkan peningkatan kebugaran jasmani lansia 0,32 poin (hasil pretest 4,4092 dan hasil postest 4,7292). Hasil uji efektifitas menunjukkan peningkatan fungsi kognitif lansia dengan 1,72 poin, (hasil pretest 25,58 dan hasil posttest 27,33). A gymnastic older model for pphysical fitness and brain function AbstractThis study aimed to develop an elderly gymnastics model for physical fitness and brain function. This research was research and development which refered to Borg & Gall's research steps through preliminary study, initial draft design analysis, expert validation, product trial, evaluation, and revision. The subjects of the small-scale trial were 15 people, the subjects of the large-scale trials were 57 people, and the subjects of the product effectiveness test were 12 people. The assessment of experts and instructors used a questionnaire sheet. The measurement of elderly physical fitness used a 6-minute walk test, while measuring their cognitive function used the Montreal Cognitive Assessment Indonesia (MoCA-Ina) test. The results showed that the developed elderly gymnastics model for physical fitness and brain function was valid. The result of expert validation showed the model was "very good" with an average score of 81.87%. The quality of the product according to the small-scale test was "excellent" with an average score of 86.25%. Furthermore, based on the large-scale trials the product was "excellent" with an average score of 98.12%. The results of the effectiveness test showed an increase in physical fitness of the elderly by 0.32 points (the pretest score of 4.4092 to the result of postest score of 4.7292). The result of the effectiveness test showed improvement of cognitive function of elderly by 1,72 point (the pretest score of 25.58 to the result of posttest score of 27.33).

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)0.003
metaresearch head score (Gemma)0.005
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 score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.003

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 head0.105
GPT teacher head0.451
Teacher spread0.346 · 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".

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Citations14
Published2018
Admission routes1
Has abstractyes

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