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Record W3111282763 · doi:10.1055/s-0040-1721554

Public’s Perception and Satisfaction on the Health Care System in Sultanate of Oman: A Cross-Sectional Study

2020· article· en· W3111282763 on OpenAlexaff
Humaid Al-Kalbani, Tariq Al‐Saadi, Ahmed Al-Kumzari, Hassan Al-Bahrani

Bibliographic record

VenueAnnals of the National Academy of Medical Sciences (India) · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsCross-sectional studyMarital statusFamily medicineMedicineHealth careGovernment (linguistics)Patient satisfactionPreferenceTertiary carePublic healthPopulationNursingEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Objective There are no “gold standard” parameters to measure patient satisfaction regarding the health care system provided by the government. Most of the developed countries have well-structured health care systems, and they depend on patient satisfaction to evaluate and optimize performance and activities of such systems. The study was conducted to evaluate the Omani population’s satisfaction toward public and private health care systems existing in the country. Materials and Methods A cross-sectional study was conducted with a predesigned and pretested questionnaire that was sent to all regions of the Sultanate of Oman via an electronic link. The questionnaire included 22 questions divided into two sections: (1) public and private health care systems in Oman, and (2) abroad treatments. Results The response rate of the 11 Oman’s governorates was 73.9%. There was an association between gender, age, marital status, and the level of education with the preference for local private hospital’s treatment (p < 0.001). Both males (88.1%) and females (83.9%) preferred to be treated by Omani doctors. The association between gender and the preference to be treated by the Omani doctors was statistically significant (p = 0.016). There was a significant relationship between the overall patient satisfaction regarding the treatment that they received and all of the following parameters: well-trained nurses, competency of doctors, professional behavior, and skill level of the staff. On the other hand, 88% of the participants were unhappy about appointment waiting times to be seen in the tertiary-care hospital. Conclusion The study showed that most of the participants have preferred to be treated by Omani physicians and nurses, however, hospitals need to make operational and working changes in order to decrease the appointment waiting time, as this was found to be one of the most common reasons for population dissatisfaction.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.432
GPT teacher head0.529
Teacher spread0.097 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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