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Record W4288077166 · doi:10.1186/s12875-022-01806-8

Coping with ‘the grey area’ of antibiotic prescribing: a theory-informed qualitative study exploring family physician perspectives on antibiotic prescribing

2022· article· en· W4288077166 on OpenAlexafffund
Michelle Simeoni, Marianne Saragosa, Celia Laur, Laura Desveaux, Kevin L. Schwartz, Noah Ivers

Bibliographic record

VenueBMC Primary Care · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMount Sinai HospitalTrillium Health CentreUniversity of TorontoPublic Health OntarioWomen's College Hospital
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsCoping (psychology)Family medicineMedicineAntibioticsQualitative researchPsychologySociologyClinical psychologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Unnecessary antibiotic use is associated with adverse side effects and rising rates of resistance at the individual and population level. This study used a theory-informed approach to identify potentially modifiable determinants of antibiotic prescribing for patients presenting to primary care with upper respiratory tract infection symptoms. METHODS: percentile). The interview guide and analysis were informed by the Theoretical Domains Framework. Each interview was coded by two research team members. Sampling and analysis continued until thematic saturation was achieved. RESULTS: Twenty family physicians were interviewed. Physicians felt that many decisions about prescribing for upper respiratory tract infection symptoms were straightforward (i.e., black and white). However, intention to avoid prescribing in cases where an antibiotic was not indicated clinically did not always align with the provider action or expectation of the patient. Clinical decisions were influenced by the Theoretical Domain Framework domains that were both internal to the physician (Knowledge, Skills, Social/Professional Role, and Belief about Capabilities) and external to the physician (Social Influence, Belief about Consequences, Reinforcement, Emotions, and Behavioural Regulation). The Environmental Context and Resources played a key role. Physicians reported significant differences in their approach to antibiotic prescribing within episodic (walk-in) or continuity of care settings, as the presence (or not) of longitudinal physician-patient relationships seemed to moderate the role of these factors on the decision-making process in cases of uncertainty. CONCLUSIONS: Antibiotic prescribing in primary care is a complex decision-making process in which context may outweigh biology during encounters featuring clinical uncertainty. Differential skill in handling uncertainty and tactics used to operationalize guideline recommendations in the real world seems to contribute to observed variation in prescribing patterns, as much or more than differences in knowledge of best practices.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.271
Teacher spread0.226 · 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.

Study designQualitative
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

Citations14
Published2022
Admission routes2
Has abstractyes

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