Evidence map›Paper›PMID 41641117›Full record

ArticleFujita medical journal2026

Development and evaluation of an ICT-based educational tool in home care nursing: A pilot qualitative study.

Keisuke Iwase, Hironori Tsuzuki, Maki Ito, Tomomi Kawakami

Abstract read
In one paragraph

Article in Fujita medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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.

Keisuke IwaseFaculty of Nursing, Fujita Health University, School of Health Sciences, Toyoake, Aichi, Japan.
Hironori TsuzukiSchool of Health Science, Department of Nursing, Toyohashi Sozo University, Toyohashi, Aichi, Japan.
Maki ItoFaculty of Nursing, Fujita Health University, School of Health Sciences, Toyoake, Aichi, Japan.
Tomomi KawakamiFaculty of Nursing, Fujita Health University, School of Health Sciences, Toyoake, Aichi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In home care nursing, nurses are required to assess not only the care recipient's condition but also their living environment. However, conventional paper-based case studies make it difficult for students to grasp the actual living environment. Objective: This study aimed to develop a learning support application (app) that combines 360-degree camera images with a paper-based case scenario and evaluate its usability among nursing students. Methods: Four areas inside the home of a single home care recipient were comprehensively photographed using a 360-degree camera. Thereafter, an app was developed to enable the users view the 360-degree images online by scanning with a smart device a QR code attached to a paper-based case scenario. To evaluate its usability, nursing students used the app and participated in semi-structured interviews. The data were analyzed using text mining techniques. Result: Text mining analysis revealed a co-occurrence network comprising four subgraphs. The representative keywords for each subgraph are as follows:Subgraph 1: Consider, Home, Fall, Risk, EnvironmentSubgraph 2: Think, Danger, Imagination, WatchSubgraph 3: Assessment, Learning support app, Residential environment, Home settings, UseSubgraph 4: Life, See, Person, Situation. Conclusion: The app supported students in understanding the home care environment and contributed to the development of care planning competence. Future studies should increase the number of cases and perform quantitative comparisons.

Indexed as

AssessmentCo-occurrence networkEnvironmentLearning support application

Identifiers

PMID41641117
PMCPMC12865279

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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.