Evidence map›Paper›PMID 41153159›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Factors Influencing Telemedicine Adoption Among Healthcare Professionals in Geriatric Medical Centers: A Technology Acceptance Model Approach.

Tammy Porat-Packer, Gizell Green, Cochava Sharon, Riki Tesler

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Tammy Porat-PackerShoham Medical Center, Pardes Hanna-Karkur 3701001, Israel.ORCID 0009-0000-4149-0227
Gizell GreenShoham Medical Center, Pardes Hanna-Karkur 3701001, Israel.
Cochava SharonDepartment of Nursing, Sha'arei Mishpat College, Margo'a St 5, Hod Hasharon 4510201, Israel.
Riki TeslerDepartment of Nursing, Faculty of Health Science, Ariel University, Ariel 4070000, Israel.

Funding

Israel National Institute for Health Policy Research. XXX
6 · The paper itself

Abstract

backgroundTelemedicine has gained significance, especially during the COVID-19 pandemic, offering remote healthcare solutions. However, its adoption in geriatric medical centers (GMCs) remains limited. Understanding the factors influencing telemedicine acceptance among care teams in geriatric medical centers is crucial for successful implementation.

aimThis study examines behavioral factors influencing telemedicine adoption among care teams in Israeli geriatric medical centers through the lens of the Technology Acceptance Model.

methodsA cross-sectional study was conducted with 406 healthcare professionals from four geriatric medical centers in Israel. Participants completed a self-administered questionnaire measuring self-efficacy, subjective norms, anxiety, resistance to change, perceived usefulness, perceived ease of use, and intention to use telemedicine. Structural equation modeling was used to analyze the data.

resultsPerceived ease of use mediated the associations between self-efficacy and perceived usefulness and between subjective norms and perceived usefulness, demonstrating how confidence shapes technology acceptance. Perceived usefulness mediated the association between perceived ease of use and intention to use. Perceived ease of use did not mediate the relationship between anxiety or resistance to technological changes and perceived usefulness.

conclusionsThe study highlights the importance of perceived ease of use and usefulness in promoting telemedicine adoption among geriatric medical center care teams, emphasizing the need for targeted interventions to enhance these perceptions.

Indexed as

healthcare professionalsorganizational behaviorself-efficacysocial influencestructural equation modelingtechnology acceptancetechnology anxietytelemedicine

Identifiers

PMID41153159
PMCPMC12561753

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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.