Evidence map›Paper›PMID 30194619›Full record

ArticleFrontiers of medicine2019

Changes in public trust in physicians: empirical evidence from China.

Dahai Zhao, Zhiruo Zhang

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Frontiers of medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
5.5field-weighted citation impact, top 4% of its field
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

22 citing papers in PubMed, 70 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Dahai ZhaoSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, 200030, China.
Zhiruo ZhangSchool of Public Health, Shanghai Jiao Tong University, Shanghai, 200025, China. zhangzhiruo@sjtu.edu.cn.
Shanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies examining the trends in public trust in physicians have provided a considerable amount of valuable policy implications for policymakers compared with cross-sectional studies on this topic in many countries. This study investigated changes in public trust in physicians in China based on two cross-sectional national surveys conducted in 2011 and 2016 and identified the determinants of these changes. The results indicated 83.4% of respondents in 2011 reported trust or strong trust in physicians in China, which decreased to 64.2% by 2016. The results of ordinal logistic regression demonstrated that public trust in physicians in China had decreased significantly from 2011 to 2016 (P< 0.001) after adjusting for other independent variables. Self-reported health status, self-rated happiness, and self-identified social class were all associated positively with public trust in physicians in China. The results also confirmed that decreasing public satisfaction with the most recent treatment experience was the major determinant of decreasing public trust in physicians in China. The findings of this study suggest that decreasing public trust in physicians deserves considerable attention from national policymakers and that improving satisfaction with treatment experiences would be the most effective strategy for enhancing public trust in physicians in China.

Indexed as

Physician-Patient RelationsTrustAdolescentAdultChinaCross-Sectional StudiesFemaleHumansLogistic ModelsMaleMiddle AgedPatient SatisfactionSurveys and QuestionnairesTaiwanYoung Adulthealth system performancephysician-patient relationshippublic trusttrust in physicians

Identifiers

PMID30194619
OpenAlexW2891555010

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.