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Record W4386370052 · doi:10.1186/s12992-023-00964-3

Medicine donations: a review of policies and practices

2023· review· en· W4386370052 on OpenAlex
Hannah Permaul Flores, Jillian Clare Köhler, Deirdre Dimancesco, Anna Wong, Joel Lexchin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGlobalization and Health · 2023
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsCentre for Disability Prevention and RehabilitationToronto Public HealthYork UniversityPublic Health OntarioUniversity of Toronto
FundersUniversity of TorontoWorld Health Organization
KeywordsDonationGovernment (linguistics)Multinational corporationRevenuePublic healthHealth policySocial policyBusinessDeveloping countryPandemicMedicineEconomic growthCoronavirus disease 2019 (COVID-19)Political scienceFinanceInfectious disease (medical specialty)EconomicsLaw

Abstract

fetched live from OpenAlex

BACKGROUND: To help promote the effective delivery of drug donations, the World Health Organization (WHO) developed the Guidelines for Medicine Donations. The need for revisions is timely given the large-scale influx of medicine donations since the start of the COVID-19 pandemic. This study analyses current policies of donors and recipients that are commensurate with the recommendations in the Guidelines and examines current practices, challenges, and revision suggestions. RESULTS: A search for medicine donation policies of donors and recipients was conducted in May/June 2022 and repeated in January 2023. Potential donor countries were identified from the high-income countries on the United Nation's (UN) List of G20 Countries. Potential pharmaceutical company donors were selected from those with 2021 revenue of $30 billion or greater. Potential non-government organization donors came from the WHO list of non-governmental organizations (NGOs) and two other sources. Potential recipient countries were those on the UN List of Least Developed Countries. These four lists were supplemented with actual donors and recipients identified from the literature. All policies retrieved were screened to identify which of the 12 recommendations from the WHO Guidelines were incorporated. We identified 38 policies from 1 donor country, 6 brand-name multinational pharmaceutical companies, 6 NGOs and 25 recipient countries. Most policies incorporated all 12 recommendations. Twenty-five of the 38 policies were developed in 2010 or later. The majority of actual donors and recipients did not have policies that were publicly available. A rapid literature review for publications from 2010 onwards identified challenges in implementing the WHO Guidelines and suggested for revisions. Challenges included: (1) information management; (2) medication presentation; (3) influence from the pharmaceutical industry; (4) donation sustainability; and (5) the belief that donations are inherently good. CONCLUSIONS: Our findings suggest that both donors and recipients could further align their policies with the existing Guidelines and both groups should be consulted on any revisions to ensure that their experiences are reflected and their needs are addressed. While the current WHO Guidelines for Medicine Donations are a solid base for medical humanitarian efforts, evidence points to the need for an update to meet current challenges.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.793
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.242
GPT teacher head0.540
Teacher spread0.298 · 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