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Record W3005474553 · doi:10.32883/hcj.v5i1.622

PENGARUH BAURAN PEMASARAN TERHADAP PROSES KEPUTUSAN PELANGGAN DALAM MEMILIH RAWAT INAP DI RUMAH SAKIT ISLAM IBNU SINA PADANG TAHUN 2012

2020· article· id· W3005474553 on OpenAlexaff
S.T. Asye Rachmawaty, Rima Semiarty, Ratni Prima Lita

Bibliographic record

VenueHuman Care Journal · 2020
Typearticle
Languageid
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsHumanitiesMedicineArt

Abstract

fetched live from OpenAlex

<p>Proses keputusan untuk memilih rawat inap bagi pelanggan bukanlah hal yang mudah. Banyak faktor yang harus dipertimbangkan untuk rawat inap di suatu rumah sakit. Penelitian ini bertujuan untuk mengetahui pengaruh bauran pemasaran terhadap keputusan pelanggan (pasien) dalam memilih rawat inap di Rumah Sakit Ibnu Sina Padang tahun 2012.</p><p>Penelitian ini adalah penelitian kuantitatif dengan metode <em>field survey</em>. Penelitian dilakukan di Rumah Sakit Ibnu Sina pada bulan Januari dan Februari 2012. Data primer dikumpulkan dengan cara menyebarkan kuisioner yang kemudian diolah dengan regresi linier berganda dalam metode enter, dimana hasil yang tidak signifikan dikeluarkan satu-persatu sampai mendapatkan variabel yang paling signifikan mempengaruhi keputusan untuk memilih dirawat inap di Rumah Sakit Ibnu Sina.</p><p>Pada penelitian ini didapatkan bahwa semua variable bauran pemasaran mempengaruhi keputusan memilih sebesar 80,5%, sedangkan sisanya dipengaruhi oleh sebab lain yang tidak ikut diteliti. Namun dari ketujuh variabel pemasaran yang diuji, ada 3 (tiga) variabel yang sangat mempengaruhi pelanggan dalam memilih. variabel tersebut, yaitu: <em>price, people, dan physical evidence</em>. Diharapkan kepada Rumah Sakit Ibnu Sina Padang untuk menurunkan biaya layanan bagi pasien sesuai dengan staus sosial ekonomi masyarakat disekitar wilayah Rumah Sakit dan meningkatkan kualitas bukti fisik layanan kesehatan seperti peralatan yang representatif, interior bangunan yang asri, eksterior bangunan, fasilitas parkir, kantin, bank, dan jaminan keamanan.</p>

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0080.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.009
Insufficient payload (model declined to judge)0.0070.001

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 head0.124
GPT teacher head0.413
Teacher spread0.289 · 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
Published2020
Admission routes1
Has abstractyes

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