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PREPARING FOR AN INFLUENZA PANDEMIC: ETHICAL ISSUES

2005· article· en· W2069554095 on OpenAlexaffabout
Jaro Kotalik

Bibliographic record

VenueBioethics · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsLakehead University
Fundersnot available
KeywordsPandemicScarcityContingency planHealth carePoliticsOrder (exchange)BusinessPublic relationsContingencyPolitical sciencePreparednessEconomic growthMedicineEconomicsCoronavirus disease 2019 (COVID-19)DiseaseLaw

Abstract

fetched live from OpenAlex

In the near future, experts predict, an influenza pandemic will likely spread throughout the world. Many countries have been creating a contingency plan in order to mitigate the severe health and social consequences of such an event. Examination of the pandemic plans of Canada, the United Kingdom and the United States, from an ethical perspective, raises several concerns. One: scarcity of human and material resources is assumed to be severe. Plans focus on prioritization but do not identify resources that would be optimally required to reduce deaths and other serious consequences. Hence, these plans do not facilitate a truly informed choice at the political level where decisions have to be made on how much to invest now in order to reduce scarcity when a pandemic occurs. Two: mass vaccination is considered to be the most important instrument for reducing the impact of infection, yet pandemic plans do not provide concrete estimates of the benefits and burdens of vaccination to assure everyone that the balance is highly favorable. Three: pandemic plans make extraordinary demands on health care workers, yet professional organizations and unions may not have been involved in the plans' formulation and they have not been assured that authorities will aim to protect and support health care workers in a way that corresponds to the demands made on them. Four: all sectors of society and all individuals will be affected by a pandemic and everyone's collaboration will be required. Yet, it appears that the various populations have been inadequately informed by occasional media reports. Hence, it is essential that plans are developed and communication programs implemented that will not only inform but also create an atmosphere of mutual trust and solidarity; qualities that at the time of a pandemic will be much needed.

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)0.141
metaresearch head score (Gemma)0.175
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.141
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1410.175
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0180.055
Scholarly communication0.0220.019
Open science0.0050.010
Research integrity0.0490.079
Insufficient payload (model declined to judge)0.0050.003

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 head0.430
GPT teacher head0.630
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations124
Published2005
Admission routes2
Has abstractyes

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