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Record W1971263403 · doi:10.1093/phe/php035

Health Inequities in Times of a Pandemic

2009· article· en· W1971263403 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePublic Health Ethics · 2009
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicMedicinePreparednessVaccinationEnvironmental healthPopulationHealth careDiseaseEconomic growthCoronavirus disease 2019 (COVID-19)Political scienceImmunologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

As of November 2009, the ongoing Influenza A H1N1 pandemic appears to be relatively mild compared to scenarios that were foreseen or feared in many pandemic preparedness plans. Currently, most H1N1 influenza patients do not experience serious complications and they recover even without treatment with antiviral drugs. Serious complications are most commonly seen among the existing risk groups for seasonal influenza, e.g., patients with pre-existing chronic respiratory diseases. However, morbidity and mortality rates among children and young adults with no previous medical history are relatively high compared to seasonal influenza. It is still uncertain how the pandemic will develop in the coming months, but one can expect that the pandemic vaccines that have become available since the beginning of November 2009 will most likely make the pandemic ‘manageable’—at least in developed countries. A number of countries like Australia, Canada and the Netherlands expect to have sufficient vaccines to immunise the whole population. However, people in other parts of the world, especially in low-income countries, may have no access to vaccination at all, despite the fact that due to socio-economic deprivation and limited access to health care, they are much more vulnerable to significant negative effects from the disease. Once again, this pandemic underlines the enormous inequities in health and in access to health care between countries.

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)0.008
metaresearch head score (Gemma)0.006
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: Commentary · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
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 integrity0.0000.002
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 head0.463
GPT teacher head0.538
Teacher spread0.074 · 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