Evidence map›Paper›PMID 38132068›Full record

ArticleHealthcare (Basel, Switzerland)2023

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust.

Bian Ted Nicholas Tan, Md Irfanuzzaman Khan, Md Abu Saleh, Dawa Wangchuk, Md Jakir Hasan Talukder, Catherine R Kinght-Agarwal

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. 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

6 authors.

Bian Ted Nicholas TanCanberra Business School, University of Canberra, Canberra 2617, Australia.ORCID 0000-0003-1084-0428
Md Irfanuzzaman KhanCanberra Business School, University of Canberra, Canberra 2617, Australia.ORCID 0000-0003-2671-7053
Md Abu SalehCanberra Business School, University of Canberra, Canberra 2617, Australia.ORCID 0000-0003-3759-1521
Dawa WangchukCanberra Business School, University of Canberra, Canberra 2617, Australia.
Md Jakir Hasan TalukderAdvance Computing and Information Science, University of South Australia, Mawson Lakes, Adelaide 5095, Australia.ORCID 0000-0001-9965-356X
Catherine R Kinght-AgarwalFaculty of Health, University of Canberra, Canberra 2617, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

Indexed as

healthcare technologyprecision medicinesocial influencetrust in doctorsUTAUT

Identifiers

PMID38132068
PMCPMC10743070

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.