Evidence map›Paper›PMID 33132811›Full record

ArticleJournal of Taibah University Medical Sciences2020

Development and validation of a Smartphone Impact Scale among healthcare professionals.

Tahani N Altamimi, Johnson Alex, Moizuddin M Khan, Binu V S Nair

Abstract read
In one paragraph

Article in Journal of Taibah University Medical Sciences, 2020. 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
–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

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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.

  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

4 authors.

Tahani N AltamimiDepartment of Family and Community Medicine, College of Medicine, Dar Al Uloom University, Riyadh, KSA.
Johnson AlexDepartment of Behavior Science, College of Medicine, Dar Al Uloom University, Riyadh, KSA.
Moizuddin M KhanDepartment of Basic Medical Sciences, College of Medicine, Dar Al Uloom University, Riyadh, KSA.
Binu V S NairDepartment of Biostatistics, National Institute of Mental Health and Neurosciences, Bengaluru, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesSmartphone overuse is prevalent among healthcare professionals. There is no standard scale that can measure the impact of smartphone usage on healthcare professionals. This study aimed to develop and validate a tool, the Smartphone Impact Scale (SIS), that can effectively measure the use of smartphone among healthcare professionals.

methodsWe developed a generic instrument to study the impact of smartphone usage among healthcare professionals. A total of 1436 healthcare professionals from various regions of KSA participated in this study through an online questionnaire-based survey. The psychometric properties of the SIS were developed using content validity index (CVI), factor analysis, internal consistency, and test-retest reliability analysis.

resultsA 23-item scale was selected for reliability and validity analysis. The average CVI was found to be 0.824. The internal consistency with Cronbach's alpha value was 0.91, and test-retest reliability was 0.85. The Cronbach's alpha values for Factors 1 and 2 were 0.875 and 0.803, respectively. The confirmatory factor analysis indices were as follows: root mean square of approximation = 0.0710, comparative fit index = 0.861, Tucker-Lewis index = 0.845, and the coefficient of determination = 0.969. The correlation between two factors was 0.66. After factor analysis, we developed a final questionnaire with 23 items.

conclusionsOur SIS showed a three-factor structure and appropriate psychometric characteristics. Due to its adequate reliability and validity, SIS can be conveniently used to evaluate the impact of smartphone usage on healthcare professionals.

Indexed as

Healthcare professionalsKSAScale developmentSmartphone addictionSmartphone scale

Identifiers

PMID33132811
PMCPMC7564985

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LicenceCC BY-NC-ND
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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.