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Record W4318701597 · doi:10.47405/mjssh.v8i1.2058

Pengukuran Kualiti Perkhidmatan: Penilaian Penyedia Kemudahan Sukan Bagi Pembangunan Atlet Majlis Sukan Negeri

2023· article· id· W4318701597 on OpenAlexaff
Azlina Zid, Siti Aishah Wahab, Siti Fadhilah Abdul Hamid, Mustakim Hashim, Hajar Asmidar Samat, Mohd Helme Basal, Hasnul Faizal Hushin Amri

Bibliographic record

VenueMalaysian Journal of Social Sciences and Humanities (MJSSH) · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsWiLAN (Canada)
FundersUniversiti Teknologi MARA
KeywordsGeneral educationPhysicsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Kualiti perkhidmatan dan kemudahan sukan dalam sesebuah organisasi sering dikaitkan dengan kesempurnaan dan kepuasan. Kemudahan sukan yang lengkap untuk kegunaan atlet dalam persediaan menghadapi kejohanan akan meningkatkan kepuasan dan prestasi ke tahap yang lebih baik. Justeru, kajian ini bertujuan mengenalpasti kualiti perkhidmatan dan kemudahan sukan yang sediakan oleh Pihak Berkuasa Tempatan (PBT) bagi program pembangunan sukan prestasi tinggi Majlis Sukan Wilayah Persekutuan (MSWP). Kajian ini menggunakan kaedah tinjauan yang melibatkan 147 responden atlet yang dipilih secara persampelan rawak. Data dari instrumen SERVQUAL (service quality) dan tahap kepuasan dianalisis menggunakan statistik deskriptif. Hasil dapatan menunjukkan kualiti responsif adalah paling tinggi dengan skor min M=3.82, SD=0.833. Manakala kualiti empati menunjukkan skor min paling rendah iaitu M=3.36, SD=0.819. Hasil dapatan kepuasan atlet terhadap kualiti kemudahan sukan yang disediakan oleh pihak PBT pula menunjukkan tahap sederhana dengan skor min M=3.66, SD=0.924. Hasil kajian ini diharapkan dapat membantu pihak pengurusan MSWP membantu PBT menambahbaik kualiti perkhidmatan dan kemudahan sukan bagi memastikan atlet mencapai prestasi cemerlang pada masa hadapan.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0080.006
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.331
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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