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Record W4414109846 · doi:10.1101/2025.09.06.25335195

Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among primary care providers: A vignette-based, pre-post pilot study in Patna, India

2025· preprint· en· W4414109846 on OpenAlexafffund
Poshan Thapa, Prachi Shukla, Chandrashekhar Joshi, Sena Sayood, P. Sinha, Diwash Timilsina, Mili Dutta, Madhukar Pai, Samira Abbasgholizadeh Rahimi, Sumanth Gandra

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsThe Quebec Population Health Research NetworkMcGill University Health CentreMcGill University
FundersMcGill University
KeywordsPrimary careAntibioticsDiarrheaMedical prescriptionIntervention (counseling)PneumoniaCellulitisTest (biology)

Abstract

fetched live from OpenAlex

Abstract Introduction Inappropriate antibiotic prescribing is a major concern in low– and middle-income countries (LMICs), particularly at the primary care level. The WHO AWaRe Antibiotic Handbook was introduced to promote rational antibiotic use, yet its real-world feasibility and potential impact remain underexplored. Our study evaluated the effectiveness and usefulness of the WHO AWaRe Handbook training among primary care providers (PCPs) in Patna, India. Methods We conducted a pre-post interventional study among 145 PCPs (40 formal providers (FPs) and 105 informal providers (IPs), 98% male) in Patna, India. Participants received training from an infectious disease physician on the WHO AWaRe Antibiotic Handbook. Antibiotic prescribing knowledge was assessed before and after the intervention using clinical vignettes for four conditions: acute diarrhea, urinary tract infection (UTI), cellulitis, and community-acquired pneumonia (CAP). An endline survey evaluated the perceived usefulness of the training. Changes in prescribing were analyzed using McNemar’s test for paired data. Results The intervention significantly reduced overall antibiotic prescribing for acute diarrhea (p=0.0003) and UTI (p=0.0113), with greater reductions among IPs. No significant changes were observed for cellulitis (p=0.3692) or CAP (p=0.7150). Watch-category antibiotic prescribing significantly decreased for acute diarrhea (p<0.0001), with no significant changes for other conditions. IPs showed greater improvements overall compared to FPs. The majority of providers (75%; n=107) rated the training as moderately or very useful. Conclusion Training PCPs using the WHO AWaRe Handbook improved antibiotic prescribing knowledge for some common conditions, particularly among IPs. Future research should focus on the impact of ongoing training, tailored interventions, and long-term follow-up. Strengths and limitations of this study – This is the first study to evaluate the effectiveness of WHO AWaRe Handbook training on improving antibiotic prescribing knowledge among primary care providers in India, focusing on both formal and informal healthcare providers. – Using a vignette-based, pre-post study design allowed for standardized assessment of prescribing knowledge across four common clinical conditions: acute diarrhea, cellulitis, pneumonia, and urinary tract infection. – Stratified analysis by provider type offered important insights into the intervention’s differential effects, particularly highlighting knowledge improvements among informal providers. – While the study captures shifts in prescribing knowledge, it does not assess actual prescribing behavior in clinical practice, which may limit the generalizability of the findings. – The study evaluated outcomes over a short follow-up period, which restricts understanding of the sustainability of training effects over time.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.283
Teacher spread0.256 · 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 designNon-randomized trial
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

Citations1
Published2025
Admission routes2
Has abstractyes

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