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Record W3047612299 · doi:10.5539/gjhs.v12n10p79

Hospital Managers’ Perceptions Regarding Setting Healthcare Priorities in Kuwait

2020· article· en· W3047612299 on OpenAlexvenueno aff
Abdullah Alsabah, Hassan Haghparast‐Bidgoli, Jolene Skordis

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsStrengths and weaknessesBusinessHealth careProcess (computing)Private sectorPublic sectorPublic relationsQualitative researchWork (physics)MarketingNursingMedicinePsychologyPolitical scienceSociologyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND & OBJECTIVE: In view of the budget limitations resulting from the downturn in the Kuwaiti economy, it is crucial to evaluate the process of priority setting within the health system to identify strengths and weaknesses of this process within both the public and private sectors. Once the weak points are identified, policy makers can work with hospital administration staff to upgrade the process with the aim of utilising health resources more efficiently. The purpose of this study is to give decision makers some insight on the perspective of hospital managers regarding the current process of priority setting, and suggest ways to improve this process. Additionally, this study will provide the opinions of hospital managers in questioning the effect of certain healthcare policies, currently given top priority, on healthcare system efficiency. The views of the hospital managers interviewed indicate their preferences in priority setting and the changes in health spending they believe are required. METHODS: A qualitative study was conducted using semi-structured, face-to-face interviews with 14 managers from public and private hospitals in Kuwait. Content analysis was used to produce major themes and sub-themes from the interview transcripts. RESULTS: While several similarities and differences in the priority-setting process between the public and private sectors were apparent, the main strength in the process that most managers from both sectors mentioned, was that it was simple, systematic, comprehensive and democratic. The several weaknesses of the process include it not being evidence-based due to the lack of accurate and up-to-date data. Also, the discrepancy between the official statements made and the actual practices of health decision makers in the country demonstrate the confusion around the priority-setting process. Most respondents, from both sectors, thought that the availability of a clear and well-communicated national health strategic plan would facilitate the necessary modifications in legislative, structural and administrative strategies to streamline the processes of allocating resources and setting priorities. For example, most respondents believed that the disadvantages of the costly practice of sending patients abroad for treatment and its effect on resource allocation outweighed its advantages. Further, the managers from both sectors had different perceptions regarding the policy of private health insurance for retirees. These two policies, according to some hospital managers, added strain to the health budget and undermined trust in the public-health sector. CONCLUSION: This study examined the perspective of hospital managers regarding the process of healthcare priority setting in Kuwait, and ways to improve it. Priority setting could be improved by having a better understanding of its strengths and weaknesses. The study concludes that health decision makers should remain responsible for accepting and implementing evidence-based, systematic processes of resource allocation. Additionally, continuous monitoring and evaluation of the impact of health policies will be required to improve overall health outcomes.        

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.099
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.039
GPT teacher head0.295
Teacher spread0.256 · 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.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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