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Record W3199788491 · doi:10.1093/heapol/czab113

Priority setting and equity in COVID-19 pandemic plans: a comparative analysis of 18 African countries

2021· article· en· W3199788491 on OpenAlexafffund
Lydia Kapiriri, Suzanne N. Kiwanuka, Godfrey Biemba, Claudia Marcela Vélez, Donya Razavi, Julia Abelson, Beverley M. Essue, Marion Danis, Susan Dorr Goold, Mariam Noorulhuda, Élysée Nouvet, Lars Sandman, Iestyn Williams

Bibliographic record

VenueHealth Policy and Planning · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsWestern UniversityCentre for Global Health ResearchSt. Michael's HospitalImpactMcMaster University
FundersMcMaster University
KeywordsEquity (law)PandemicPreparednessContext (archaeology)Public healthBusinessQuality (philosophy)Health careMedicineEconomic growthActuarial scienceCoronavirus disease 2019 (COVID-19)Political scienceGeographyDiseaseEconomicsInfectious disease (medical specialty)Nursing

Abstract

fetched live from OpenAlex

Priority setting represents an even bigger challenge during public health emergencies than routine times. This is because such emergencies compete with routine programmes for the available health resources, strain health systems and shift health-care attention and resources towards containing the spread of the epidemic and treating those that fall seriously ill. This paper is part of a larger global study, the aim of which is to evaluate the degree to which national COVID-19 preparedness and response plans incorporated priority setting concepts. It provides important insights into what and how priority decisions were made in the context of a pandemic. Specifically, with a focus on a sample of 18 African countries' pandemic plans, the paper aims to: (1) explore the degree to which the documented priority setting processes adhere to established quality indicators of effective priority setting and (2) examine if there is a relationship between the number of quality indicators present in the pandemic plans and the country's economic context, health system and prior experiences with disease outbreaks. All the reviewed plans contained some aspects of expected priority setting processes but none of the national plans addressed all quality parameters. Most of the parameters were mentioned by less than 10 of the 18 country plans reviewed, and several plans identified one or two aspects of fair priority setting processes. Very few plans identified equity as a criterion for priority setting. Since the parameters are relevant to the quality of priority setting that is implemented during public health emergencies and most of the countries have pre-existing pandemic plans; it would be advisable that, for the future (if not already happening), countries consider priority setting as a critical part of their routine health emergency and disease outbreak plans. Such an approach would ensure that priority setting is integral to pandemic planning, response and recovery.

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)0.008
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.313
GPT teacher head0.587
Teacher spread0.275 · 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

Citations26
Published2021
Admission routes2
Has abstractyes

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