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Record W3049670251 · doi:10.1177/0020731420949823

The Antimicrobial Resistance Crisis: How Neoliberalism Helps Microbes Dodge Our Drugs

2020· article· en· W3049670251 on OpenAlexaff
Ilinca A. Dutescu

Bibliographic record

VenueInternational Journal of Health Services · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsYork University
Fundersnot available
KeywordsNeoliberalism (international relations)PoliticsIdeologyPolitical scienceCorporate governancePublic healthDevelopment economicsEconomic growthPolitical economySociologyMedicineEconomicsLaw

Abstract

fetched live from OpenAlex

The urgent public health threat of antimicrobial resistance (AMR) has received much attention from the world's most important health agencies and national governmental organizations. However, despite large investments being allocated to strategizing national and international plans for addressing this public health problem, the incidence of untreatable, antimicrobial-resistant diseases continues to rise in many nations. To avoid returning to a society in which common infections once again become deadly, one must consider the often-ignored root causes driving inappropriate behaviors relating to antimicrobial use, such as the history of antimicrobial drug development, the effects of commodifying health-related services, and the rise in social inequalities. By employing the lens of political economy to analyze the phenomenon of AMR on national and international scales, it is found that the acceptance of neoliberalism as a governing ideology by authorities is hindering our ability to globally combat AMR through the depoliticization of issues that require political intervention to stimulate change. Differences in level of AMR and approaches to pharmaceutical governance between social democratic and liberal welfare states provide validity to this hypothesis.

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 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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2020
Admission routes1
Has abstractyes

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