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Record W2981487348

Canadian physicians' knowledge and counseling practices related to antibiotic use and antimicrobial resistance: Two-cycle national survey.

2017· article· en· W2981487348 on OpenAlexaffabout
Courtney R. Smith, Lisa Pogany, Simon N. Foley, Jun Wu, K Timmerman, M Gale-Rowe, Alain Demers

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsTreasury Board of Canada SecretariatPublic Health Agency of Canada
Fundersnot available
KeywordsMedicineAntibioticsFamily medicineAntibiotic resistanceOdds ratioOddsHygieneCross-sectional studyInfection controlEnvironmental healthInternal medicineIntensive care medicineLogistic regression
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To establish a baseline for physicians' knowledge of and counseling practices on the use of antibiotics and antimicrobial resistance (AMR), and to determine potential changes in these measures after the implementation of a national AMR awareness campaign. DESIGN: Cross-sectional design. SETTING: Canada. PARTICIPANTS: A total of 1600 physicians. MAIN OUTCOME MEASURES: Physicians' knowledge of and counseling practices on antibiotic use and AMR at baseline and after implementation of the AMR awareness campaign. RESULTS: = .01]). Most respondents in both surveys reported feeling confident with respect to counseling their patients on the appropriate use of antibiotics and AMR. CONCLUSION: Physicians' knowledge of and levels of counseling on the use of antibiotics and AMR were high and fairly stable in both survey results. This shows that Canadian physicians are demonstrating behaviour patterns of AMR stewardship. Existing gaps in counseling practices might be a result of physicians believing that pharmacists or nurses are addressing these issues with patients. Future national surveys conducted among pharmacists and nurses would contribute to the evidence base for AMR stewardship activities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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 designObservational
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

Citations22
Published2017
Admission routes2
Has abstractyes

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