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Record W2462512219 · doi:10.1097/md.0000000000003803

Antibiotic prescribing of village doctors for children under 15 years with upper respiratory tract infections in rural China

2016· article· en· W2462512219 on OpenAlexaff
Zhixia Zhang, Xingxin Zhan, Hongjun Zhou, Fang Sun, Heng Zhang, Merrick Zwarenstein, Qian Liu, Yingxue Li, Weirong Yan

Bibliographic record

VenueMedicine · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineMedical prescriptionFamily medicineFocus groupRespiratory tract infectionsChinaThematic analysisAntibioticsPublic healthPediatricsEnvironmental healthNursingQualitative researchInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study was to explore the knowledge, attitudes, and practices of village doctors regarding the prescribing of antibiotics for children under 15 years with upper respiratory tract infections (URTIs) in rural China. Twelve focus group discussions (FGDs) were conducted in Xianning, a prefecture-level city in rural China, during December 2014. We conducted 6 FGDs with 35 village doctors, 3 with 13 primary caregivers (11 parents), and 3 with 17 directors of township hospitals, county-level health bureaus, county-level Centers for Disease Control and Prevention, or county-level Chinese Food and Drug Administration offices. Audio records of the interviews were transcribed verbatim and analyzed using the thematic analysis approach. Participants believed that unnecessary antibiotic prescribing for children under 15 years with The occurrence of URTIs was a problem in village clinics in rural China. The discussions revealed that most of the village doctors had inadequate knowledge and misconceptions about antibiotic use, which was an important factor in the unnecessary prescribing. Village doctors and directors reported that the doctors' fear of complications, the primary caregivers' pressure for antibiotic treatment, and the financial considerations of patient retention were the main factors influencing the decision to prescribe antibiotics. Most of the primary caregivers insisted on antibiotics, even when the village doctors were reluctant to prescribe them, and they preferred to go to see those village doctors who prescribed antibiotics. The interviewees also gave their opinions on what would be the most effective measures for optimizing antibiotic prescriptions; these included educational/training campaigns, strict regulations on antibiotic prescription, and improved supervision. Findings emphasized the need to improve the dissemination of information and training/education, and implement legislation on the rational use of antibiotics. And it also provided helpful information to guide the design of more effective interventions to promote prudent antibiotic use and good antimicrobial stewardship.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

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