Evidence map›Paper›PMID 41896496›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

E-Learning Acceptance among Oncology Healthcare Professionals in Europe.

Taibe Kulaksız, Jana Steinbacher, Marlene Ganz, Max Klein, Marco Kalz

Abstract read
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In one paragraph

Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 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

5 authors.

Taibe KulaksızHeidelberg University of Education, Im Neuenheimer Feld 561, 69120, Heidelberg, Germany.
Jana SteinbacherInstitut für Bildungsanalysen Baden-Württemberg, Stuttgart, Germany.
Marlene GanzHeidelberg University of Education, Im Neuenheimer Feld 561, 69120, Heidelberg, Germany. ganz@ph-heidelberg.de.ORCID http://orcid.org/0009-0004-4185-9898
Max KleinHeidelberg University of Education, Im Neuenheimer Feld 561, 69120, Heidelberg, Germany.
Marco KalzHeidelberg University of Education, Im Neuenheimer Feld 561, 69120, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), this study investigates factors influencing oncology healthcare professionals’ intentions to use e-learning within an inter-specialty training program. A cross-sectional online survey was conducted across EU/EEA/UK countries with 67 educators and 97 learners. Adapted UTAUT-based measures assessed performance expectancy, effort expectancy, facilitating conditions, attitudes toward e-learning, self-efficacy, and behavioral intention. Multiple regression analyses revealed that for educators, attitudes toward e-learning were the only significant predictor of behavioral intention to use e-learning for teaching, explaining 75.2% of the variance. For learners, attitudes toward e-learning and performance expectancy significantly predicted behavioral intention to use e-learning, explaining 60.6% of variance. These findings highlight attitudes toward e-learning as the strongest determinant of e-learning acceptance. Institutions should foster positive technology attitudes through supportive environments, awareness initiatives, and incentives to enhance digital learning adoption in oncology education.

Indexed as

E-learningInter-specialty trainingOncology educationTechnology acceptanceUTAUT

Identifiers

What OpenQuestion holds

Textmetadata
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.