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Record W2070052346 · doi:10.5539/gjhs.v5n5p1

Factors Influencing Support for National Health Insurance among Patients Attending Specialist Clinics in Malaysia

2013· article· en· W2070052346 on OpenAlexvenueno aff
Yasmin Almualm, Sharifa Ezat Alkaff, Syed Mohamed Aljunid, Syed Sagoff Alsagoff

Bibliographic record

VenueGlobal Journal of Health Science · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsNational health insuranceRespondentFamily medicineBusinessHealth insuranceHealth careEnvironmental healthMedicineEconomic growthPopulationPolitical science

Abstract

fetched live from OpenAlex

This study was carried out to determine the level of support towards the proposed National Health Insurance scheme among Malaysian patients attending specialist clinics at the National University of Malaysia Medical centre and its influencing factors. The cross sectional study was carried out from July-October 2012. 260 patients were selected using multistage sampling method. 71.2% of respondents supported the proposed National Health insurance scheme. 61.4% of respondents are willing to pay up to RM240 per year to join the National Health Insurance and 76.6% of respondents are of the view that enrollment in NHI should be made compulsory. Knowledge had a positive influence on respondent's support towards National Health Insurance. National Health Insurance when implemented in Malaysia can be used to raise funds for health care financing, increase access to health services and achieve the desired health status. More efforts should be taken to promote the scheme and educate the public in order to achieve higher support towards the proposed National Health Insurance. The cost to enroll in NHI as well as services to be included under the scheme should be duly considered.

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)0.005
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 score0.005
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.065
GPT teacher head0.336
Teacher spread0.271 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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