Evidence map›Paper›PMID 35432122›Full record

ArticleFrontiers in psychology2022

Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities.

Xiaoling Wei, Yuan-Teng Hsu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
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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.

Xiaoling WeiCollege of International Business, Zhejiang Yuexiu University, Zhejiang, China.
Yuan-Teng HsuResearch Center of Finance, Shanghai Business School, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the non-English text to obtain more doctor's latent characteristics. We found five of the most frequently mentioned topics. In addition to the first topic (doctor's academic rank and practice name), "research ability," "foreign experience," "committee position," and "clinical experience" were included as unstructured descriptions in the doctor's profile. Inferences about physician ratings and page views could be improved if these themes were set as characteristics of physicians. Specifically, in our findings, Physicians' mentions of their "research ability" and "foreign experience" had a significant positive impact on physician ratings. Surprisingly, physicians mentioning more "clinical experience" had a significant negative impact on physician ratings. Moreover, while descriptions about "foreign experience" and "committee position" had a significant positive impact on page views, physician mentions of "research ability" had a significant negative impact on page views. These results provide new insights into the ways in which online healthcare community managers or physicians create their personal online profiles.

Indexed as

online healthcare communitypage viewsratingstext miningtopic modeling

Identifiers

PMID35432122
PMCPMC9010736

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.