Evidence map›Paper›PMID 34654368›Full record

ArticleBMC family practice2021

Exploring antibiotic prescribing in public and private primary care settings in Singapore: a qualitative analysis informing theory and evidence-based planning for value-driven intervention design.

Huiling Guo, Zoe Jane-Lara Hildon, Victor Weng Keong Loh, Meena Sundram, Muhamad Alif Bin Ibrahim, Wern Ee Tang, Angela Chow

Abstract read
In one paragraph

Article in BMC family practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Huiling GuoDepartment of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.
Zoe Jane-Lara HildonSaw Swee Hock School of Public Health, National University of Singapore and National University Health Systems, Singapore, Singapore. Zhildon@nus.edu.sg.
Victor Weng Keong LohDivision of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Meena SundramNational University Polyclinics, Singapore, Singapore.
Muhamad Alif Bin IbrahimDepartment of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.
Wern Ee TangNational Healthcare Group Polyclinics, Singapore, Singapore.
Angela ChowDepartment of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSingapore's healthcare system presents an ideal context to learn from diverse public and private operational models and funding systems.

aimTo explore processes underpinning decision-making for antibiotic prescribing, by considering doctors' experiences in different primary care settings.

methodsThirty semi-structured interviews were conducted with 17 doctors working in publicly funded primary care clinics (polyclinics) and 13 general practitioners (GP) working in private practices (solo, small and large). Data were analysed using applied thematic analysis following realist principles, synthesised into a theoretical model, informing solutions to appropriate antibiotic prescribing.

resultsGiven Singapore's lack of national guidelines for antibiotic prescribing in primary care, practices are currently non-standardised. Themes contributing to optimal prescribing related first and foremost to personal valuing of reduction in antimicrobial resistance (AMR) which was enabled further by organisational culture creating and sustaining such values, and if patients were convinced of these too. Building trusting patient-doctor relationships, supported by reasonable patient loads among other factors were consistently observed to allow shared decision-making enabling optimal prescribing. Transparency and applying data to inform practice was a minority theme, nevertheless underpinning all levels of optimal care delivery. These themes are synthesised into the VALUE model proposed for guiding interventions to improve antibiotic prescribing practices. These should aim to reinforce intrapersonal Values consistent with prioritising AMR reduction, and Aligning organisational culture to these by leveraging standardised guidelines and interpersonal intervention tools. Such interventions should account for the wider systemic constraints experienced in publicly funded high patient turnover institutions, or private clinics with transactional models of care. Thus, ultimately a focus on Liaison between patient and doctor is crucial. For instance, building in adequate consultation time and props as discussion aids, or quick turnover communication tools in time-constrained settings. Message consistency will ultimately improve trust, helping to enable shared decision-making. Lastly, Use of monitoring data to track and Evaluate antibiotic prescribing using meaningful indicators, that account for the role of shared decision-making can also be leveraged for change.

conclusionsThese VALUE dimensions are recommended as potentially transferable to diverse contexts, and the model as implementation tool to be tested empirically and updated accordingly.

Indexed as

Anti-Bacterial AgentsGeneral PractitionersDecision Making, SharedHumansPrimary Health CareSingaporeAnti-Bacterial AgentsAntibiotic prescribingAntimicrobial stewardshipPrimary health care doctorsQualitative researchVALUE model for appropriate antibiotic prescribing in primary care

Identifiers

PMID34654368
PMCPMC8519324

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.