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Record W2338774974 · doi:10.5539/ass.v12n5p167

Patients’ Satisfactions from Public Hospitals Services in Alkharj and Hotat Bani Tamim: A Comparative Study

2016· article· en· W2338774974 on OpenAlexvenueno aff
Mohammad Tariq Intezar, Khalid Abdullah Alotaibi, Ahmed Saied Rahama Abdallah

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersDeanship of Scientific Research, Prince Sattam bin Abdulaziz UniversityPrince Sattam bin Abdulaziz University
KeywordsRasch modelHealth careSample (material)Family medicinePublic hospitalHospital careMedicineNursingPsychologyPolitical science

Abstract

fetched live from OpenAlex

Patients’ satisfaction at public hospitals is an integral part to any hospital in the world. In recent times, health care industry has restructured its services to give better health care services to the study on patients. The focus of the study on patient’ satisfaction from public hospitals services in selected cities Alkharj and Hotat Bani Tamim of Riyadh regions, Saudi Arabia. In these two public hospitals from each city, we conducted a sample of two hundred nineteen patients including hospitals representatives selected to collect primary data using Rasch measurement model, the measured items with the goodness fit and misfit of data. In the public hospitals services five items fulfilled the three stipulated criteria for misfit while two diagnosed as minor misfits. Patients’ satisfaction from the two hospitals none of the items fulfilled the three stipulated criteria for misfit is due to varies of response from the respondents. The study shows that public hospitals are not performing well and hospitals services are inappropriate according to the needs of hospital representatives.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.275
Teacher spread0.250 · 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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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