Evidence map›Paper›PMID 37538265›Full record

ArticleFrontiers in public health2023

Determining factors affecting the user's intention to disclose privacy in online health communities: a dual-calculus model.

Zhuo Sun, Guoquan Zang, Zongshui Wang, Shuang Ge, Wei Liu, Kaiyang Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
6.4field-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

1 citing paper in PubMed, 11 citations in OpenAlex.

  1. 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

6 authors at 5 institutions in 1 country.

Zhuo SunSchool of Information Management, Zhengzhou University, Zhengzhou, China.
Guoquan ZangSchool of Information Management, Zhengzhou University, Zhengzhou, China.
Zongshui WangSchool of Economics and Management, Beijing Information S&T University, Beijing, China.
Shuang GeBusiness School, China University of Political Science and Law, Beijing, China.
Wei LiuSchool of Economics and Management, China University of Petroleum (Huadong), Qingdao, China.
Kaiyang WangBusiness School, Zhengzhou University, Zhengzhou, China.
Zhengzhou University · CNBeijing Information Science & Technology University · CNChina University of Petroleum, East China · CNChina University of Political Science and Law · CNZhengzhou Business University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As a new type of medical service application for doctor-patient interaction, online health communities (OHCs) have alleviated the imbalance between the supply and demand of medical resources in different regions and the problems of "difficult and expensive access to medical care", but also raised the concern of patients about the risk of disclosure of their health privacy information. Methods: In this study, a dual-calculus model was developed to explore users' motivation and decision-making mechanism in disclosing privacy information in OHCs by combining risk calculus and privacy calculus theories. Results: In OHCs, users' trust in physicians and applications is a prerequisite for their willingness to disclose health information. Meanwhile, during the privacy calculation, users' perceived benefits in OHCs had a positive effect on both trust in doctors and trust in applications, while perceived risks had a negative effect on both trusts in doctors and trust in applications. Furthermore, in the risk calculation, the perceived threat assessment in OHCs had a significant positive effect on perceived risk, while the response assessment had a significant negative effect on perceived risk, and the effect of users' trust in physicians far exceeded the effect of trust in applications. Finally, users' trust in physicians/applications is a mediating effect between perceived benefits/risks and privacy disclosure intentions. Conclusion: We combine risk calculus and privacy calculus theories to construct a dual-calculus model, which divides trust into trust in physicians and trust in applications, in order to explore the intrinsic motivation and decision-making mechanism of users' participation in privacy disclosure in OHCs. On the one hand, this theoretically compensates for the fact that privacy computing often underestimates perceived risk, complements the research on trust in OHCs, and reveals the influencing factors and decision transmission mechanisms of user privacy disclosure in OHCs. On the other hand, it also provides guidance for developing reasonable privacy policies and health information protection mechanisms for platform developers of OHCs.

Indexed as

PhysiciansPrivacyDisclosureHumansIntentionPatientsdual-calculus modelonline health communitiesprivacy calculusprivacy disclosurerisk calculus

Identifiers

PMID37538265
PMCPMC10394383
OpenAlexW4384826909

What OpenQuestion holds

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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.