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Record W2577922392 · doi:10.5539/ijms.v9n1p129

The Impact of Stakeholders on Health Services Development: An Empirical Investigation on the Surgical Department at King Fahd General Hospital, Saudi Arabia

2017· article· en· W2577922392 on OpenAlexvenueno aff
Alaeddin Ahmad

Bibliographic record

VenueInternational Journal of Marketing Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisBusinessGovernment (linguistics)Sample (material)MarketingService (business)StakeholderPublic healthService providerEmpirical researchPopulationPublic relationsMedicineEnvironmental healthNursingPolitical science

Abstract

fetched live from OpenAlex

The current research investigates the stakeholders influencing health services development at King Fahd General Hospital KFGH in Jeddah city, Saudi Arabia. This study proposes and tests a six factors model that influences health services development. These factors include government regulations, competitors, suppliers, patients, public, and health service providers as independent variables; the dependent variable is health services development. In order to explore this issue, a quantitative method was used to collect primary data through a questionnaire, which was administered in KFGH in Jeddah city in Kingdom of Saudi Arabia. The researches targeted 141 surgeons in this research as a sample because of the small population. A purposive sample was used to choose the participants in this research. The research retrieves 130 valid questionnaires; representing 92%.The results confirm significant differences in the influence of these factors on health service development. The research concludes that there is a significant influence of governmental regulations, competitors, suppliers, patients, public, and health service providers on health services development. The research recommends enhancing the awareness of stakeholder factors by studying the effects of governmental regulations, competitors, suppliers, patients, public, and health service providers. The last is adopting and updating medical and non-medical technology to maintain health service development.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.141
GPT teacher head0.423
Teacher spread0.282 · 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 teacher head, not a consensus.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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