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Record W3030380999 · doi:10.3390/antibiotics9060285

Antimicrobial Resistance in Humans and Animals: Rapid Review of Psychological and Behavioral Determinants

2020· review· en· W3030380999 on OpenAlexfundno aff
Julie A. Chambers, Margaret Crumlish, David Comerford, Ronan E. O’Carroll

Bibliographic record

VenueAntibiotics · 2020
Typereview
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsPsychological interventionResistance (ecology)Promotion (chess)PopulationBehavior changePsychologyMedicineEnvironmental healthSocial psychologyEcologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

A rapid review of current evidence examining psychological issues regarding the use of antibiotics and antimicrobials and resistance to these in both human and animal populations was conducted. Specific areas of interest were studies examining psychological determinants of AMR and interventions which attempt to change behavior with regard to AMR in the general population; animals; and fish, in particular. Although there is some evidence of the effectiveness of behavior change in general human populations, there is limited evidence in farmed animals, with a particular dearth in fish farming. We conclude there is an urgent need for more psychological research to identify major barriers and facilitators to change and evaluate the effectiveness of theory-based interventions aimed at reducing AM use in food production animals, including the promotion of alternatives to AMs, such as vaccination.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.212
GPT teacher head0.506
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2020
Admission routes1
Has abstractyes

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