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Record W2617563952

Medication access via hospital admission.

2017· article· en· W2617563952 on OpenAlexaffabout
Annie Wang, Trudo Lemmens, Nav Persaud

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedical prescriptionMedicineObligationHealth careFamily medicineHospital admissionMedical emergencyNursingLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Access to appropriate medications is an important determinant of health. Canada is one of a few countries in the world where outpatients generally pay for medications while medications administered to hospital inpatients are publicly funded. As a result, a significant portion of Canadians does not adhere to treatment regimens because of difficulty paying for medications. Patients are more likely to fill prescriptions if they are not charged. Providing access to medications without charge is known to improve health outcomes and reduce mortality. Under provincial laws, physicians with hospital admitting privileges generally decide which patients should be admitted to hospital based on the level of care they require. In this paper, the authors put forward that to provide patients with long-term access to medications, physicians could lawfully admit those patients to the hospital, administer the medications, and then grant them a leave of absence from the hospital until they require a medication refill. These patients would not occupy a bed in the hospital, yet would have access to essential medications they would not otherwise be able to afford. The paper explores the ethical, professional, and legal implications of admitting outpatients to the hospital for the sole purpose of providing them with medications that, according to the Canada Health Act, must be provided without charge to inpatients. The authors conclude the practice is not only consistent with accepted standards for physicians and with federal and provincial laws, but could also be seen as an ethical, professional and human rights obligation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.061
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0610.003

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 head0.092
GPT teacher head0.296
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes2
Has abstractyes

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