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Record W2088286185 · doi:10.1586/14737167.5.2.215

Health insurance, access to prescription medicines and health outcomes in children

2005· article· en· W2088286185 on OpenAlexafffund
Wendy J. Ungar, Rinat Ariely

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2005
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health ResearchNovartis PharmaRAND CorporationNovartis
KeywordsMedical prescriptionReceiptCost sharingMedicineFamily medicinePrescription costsHealth carePharmacyHealth insuranceBusinessEnvironmental healthPrescription drugNursingEconomic growthAccounting

Abstract

fetched live from OpenAlex

Ensuring optimal access to medications has received increasing attention as healthcare systems struggle with increasing costs. Although this has been studied extensively in adults, there has been little investigation in pediatric populations, which have different healthcare needs. A literature review was conducted to examine the evidence regarding the relationship between insurance-mediated access to prescription medicines and outcomes in children. In total, 12 studies were classified according to uninsured versus insured, type of insurance provider and impact of family income. The studies demonstrated that insurance coverage and low-cost sharing are both essential to facilitate access to medications. Increased access was consistently observed for insured compared with uninsured children. Access to prescription drugs frequently differed by type of health provider organization. Adequate family income was an important determinant of access to and receipt of prescriptions. Moreover, income-indexed insurance coverage may increase unmet need. Compared with the literature on access to prescription medicines and health outcomes in adults, there have been few studies in children. Further research relating pharmaceutical policies to pediatric health outcomes is needed to strengthen the quality of policy decision making regarding access to prescription medicines for children.

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)0.004
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: Review · Consensus signal: none
Teacher disagreement score0.410
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.144
GPT teacher head0.621
Teacher spread0.477 · 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
GenreReview

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

Citations10
Published2005
Admission routes2
Has abstractyes

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