Evidence map›Paper›PMID 31518237›Full record

ArticleJMIR aging2018

Identifying Consumers Who Search for Long-Term Care on the Web: Latent Class Analysis.

Darren Liu, Takashi Yamashita, Betty Burston

Abstract read
In one paragraph

Article in JMIR aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Darren LiuDepartment of Public Health, College of Health Sciences, Des Moines University, Des Moines, IA, United States.ORCID http://orcid.org/0000-0003-0301-4080
Takashi YamashitaDepartment of Sociology, Anthropology, and Health Administration and Policy, College of Arts, Humanities, and Social Sciences, University of Maryland, Baltimore County, Baltimore, MD, United States.ORCID http://orcid.org/0000-0003-2325-126X
Betty BurstonDepartment of Health Care Administration and Policy, School of Community Health Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States.ORCID http://orcid.org/0000-0002-7954-3539

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBecause the internet has become a primary means of communication in the long-term care (LTC) and health care industry, an elevated understanding of market segmentation among LTC consumers is an indispensable step to responding to the informational needs of consumers.

objectiveThis exploratory study was designed to identify underlying market segments of the LTC consumers who seek Web-based information.

methodsData on US adult internet users (n=2018) were derived from 2010 Pew Internet and America Life Project. Latent class analysis was employed to identify underlying market segments of LTC Web-based information seekers.

resultsWeb-based LTC information seekers were classified into the following 2 subgroups: heavy and light Web-based information seekers. Overall, 1 in 4 heavy Web-based information seekers used the internet for LTC information, whereas only 2% of the light information seekers did so. The heavy information seekers were also significantly more likely than light users to search the internet for all other health information, such as a specific disease and treatment and medical facilities. The heavy Web-based information seekers were more likely to be younger, female, highly educated, chronic disease patients, caregivers, and frequent internet users in general than the light Web-based information seekers.

conclusionsTo effectively communicate with their consumers, providers who target Web-based LTC information seekers can more carefully align their informational offerings with the specific needs of each subsegment of LTC markets.

Indexed as

consumer health informationinformation seeking behaviorinternetmarketing of health servicespublic reporting

Identifiers

PMID31518237
PMCPMC6715101

What OpenQuestion holds

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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.