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Record W2058407374 · doi:10.1093/jac/48.3.435

Antibiotics and shared decision-making in primary care

2001· article· en· W2058407374 on OpenAlexaff
Christopher Butler

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
FundersMedical Research CouncilOffice of Research and Development
KeywordsPaternalismPsychological interventionRespiratory tract infectionsIntensive care medicinePrimary careMedicineAntibioticsNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Antibiotics are often prescribed to patients with respiratory tract infections who are unlikely to benefit. Models of physician-patient interaction may help understanding of this problem and inform the design of communication skills interventions to enhance appropriate prescribing. The 'paternalistic model' of the consultation remains common in the setting of acute respiratory tract infections. However, the four assumptions that could support this model are not valid for most of these patients, because: best treatment is controversial; management is inconsistent; physicians are not in the best position to evaluate trade-offs between management options without understanding patients' perspectives; and many pressures (apart from patients' agendas) intrude into the consultation. One alternative is the 'informed model' of consulting, but this does not take society's interests into account. The 'shared decision-making model', however, provides a framework for addressing both clinicians' and patients' agendas, and could guide the development and evaluation of specific consultation strategies to promote more appropriate use of antibiotics in primary care.

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)0.038
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.085
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.018
Scholarly communication0.0100.005
Open science0.0020.010
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.065
GPT teacher head0.385
Teacher spread0.320 · 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 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

Citations111
Published2001
Admission routes1
Has abstractyes

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