Evidence map›Paper›PMID 36011208›Full record

ArticleHealthcare (Basel, Switzerland)2022

Study on the Relationships between Doctor Characteristics and Online Consultation Volume in the Online Medical Community.

Zhu Chen, Qingli Song, An Wang, Dong Xie, Huiying Qi

Open access · goldAbstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

5 authors at 1 institution in 1 country.

Zhu ChenSchool of Nursing, Peking University, Beijing 100191, China.ORCID 0000-0002-4392-2916
Qingli SongSchool of Nursing, Peking University, Beijing 100191, China.ORCID 0000-0002-2729-2731
An WangSchool of Nursing, Peking University, Beijing 100191, China.ORCID 0000-0002-2644-4924
Dong XieSchool of Basic Medical Sciences, Peking University, Beijing 100191, China.
Huiying QiDepartment of Health Informatics and Management, School of Health Humanities, Peking University, Beijing 100191, China.ORCID 0000-0003-4075-3720
Peking University · CN

Funding

Innovation experiment project for university students, Peking University Health Science Center 73201Y1162
6 · The paper itself

Abstract

backgroundAs a new medical service model, the online health community can integrate various medical resources to the maximum extent and improve the accessibility and utilization rate of hospital high-quality medical resources.

objectiveResearch based on the characteristics of doctors can enable doctors to display themselves on the network platform better, provide better services for patients, and improve the quality of medical services for doctors.

methodBy crawling the characteristic data of doctors in Good Doctor Online, using dynamic analysis, correlation analysis and regression analysis, this study explores the relationships between each characteristic data and online consultation volume.

resultsThe doctor's title and city level representing the static characteristics of the doctor have a weak impact on the doctor's online consultation volume, and the doctor's dynamic characteristics such as the number of patient completions, the number of gifts received, and the number of published articles can have a positive impact on the doctor's online consultation volume. However, the recommended heat will negatively affect the online consultation volume, and the comment text has no significant impact on the doctor's online consultation volume.

conclusionTherefore, doctors should actively publicize and show their professional level and constantly optimize their dynamic characteristics, increasing the number of online consultations and thus improving their influence.

Indexed as

correlation analysisdoctor characteristicemotional analysisonline consultation volumeonline health communityregression analysis

Identifiers

PMID36011208
PMCPMC9408720
OpenAlexW4292295133

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.