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Record W3164082249 · doi:10.1515/apjri-2019-0020

Employment Rank and the Choice of Health Insurance Benefit Scheme among Bangladeshi Civil Servants

2021· article· en· W3164082249 on OpenAlexaff
Syed Abdul Hamid, Afroza Begum, Syed M. Ahsan, Sushil Ranjan Howlader, Azhar Uddin, Taslima Rahman, Hafizur Rahman

Bibliographic record

VenueAsia-Pacific Journal of Risk and Insurance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsConcordia University
Fundersnot available
KeywordsSalaryExchequerSubsidyCivil servantsPreferenceGovernment (linguistics)Rank (graph theory)Actuarial scienceCost sharingWillingness to payEconomicsRegretState (computer science)Join (topology)BusinessDemographic economicsLabour economicsMicroeconomicsPolitical sciencePoliticsStatistics

Abstract

fetched live from OpenAlex

Abstract This study surveys 622 Bangladeshi civil servants of all administrative jurisdictions and elicits their preference for health insurance schemes. The latter vary in the amount of sum assured as well as in terms of premium sharing rules with the government. The paper also explores the financial burden that the premium subsidy may impose on the exchequer and the state’s fiscal capacity to shoulder it. We discover a very high willingness to join the scheme. Though all three premium-sharing options posit flat rates common for all employment ranks, respondents appear to prefer premiums proportional to their basic salary.

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)0.002
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 score0.040
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.241
Teacher spread0.218 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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