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Consumption of antibiotics in a small Pacific island nation: Samoa

2007· article· en· W2129745509 on OpenAlexaboutno aff
Pauline Norris, Hong Anh Thi Nguyen

Bibliographic record

VenuePharmacy Practice · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
FundersHealth Research Council of New ZealandUniversity of Otago
KeywordsMedicineMedical prescriptionAntibioticsPharmacyEnvironmental healthHospital pharmacyQuarter (Canadian coin)Consumption (sociology)Antibiotic resistanceFamily medicineGeographyNursingMicrobiology

Abstract

fetched live from OpenAlex

High levels of antibiotic use contribute to development of antibiotic resistance. There is little known about levels of antibiotic use in Samoa, although anecdotally, there are high levels of use, and a strain of methicillin-resistant Staphylococcus aureus may have developed there. The study aimed to gather basic data on levels of antibiotic use in Samoa. All those who import medicines into Samoa were interviewed; invoices, prescription records in hospitals, pharmacies and health centres were reviewed; and prospective observation was carried out in private pharmacies. Analysis of orders made in one year provided an estimate of overall antibiotic consumption of 37.3 Defined Daily Doses (DDDs) per 1000 inhabitant days. Penicillins comprised 63% of DDDs used. Antibiotics were around a third of all prescribed drugs in hospitals and pharmacies, and 44% of those dispensed in health centres. Approximately two-thirds of prescriptions dispensed included an antibiotic. A quarter of antibiotic sales in pharmacies were without a prescription. Samoa has high rates of use of antibiotics and very high reliance on penicillins, compared to other developing countries. Levels of prescribing are high compared with other developing nations. It is feasible to calculate total consumption of medicines in very small developing nations.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.048
GPT teacher head0.333
Teacher spread0.285 · 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 designBench or experimental
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

Citations15
Published2007
Admission routes1
Has abstractyes

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