ArticleInternational journal of environmental research and public health2022
Effect of Digital-Based Self-Learned Educational Intervention about COVID-19 Using Protection Motivation Theory on Non-Health Students' Knowledge and Self-Protective Behaviors at Saudi Electronic University.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Article
- Article
- The effect of a self-learned virtual learning package on knowledge, attitude, and self-care behaviors of COVID-19 in people referred to health and treatment centers.BMC public health · 2024Article
- Meta-analysis on application of Protection Motivation Theory in preventive behaviors against COVID-19.International journal of disaster risk reduction : IJDRR · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe COVID-19 pandemic has disastrous impacts that impose the cultivation of knowledge and motivation of self-protection to foster disease containment.
aimEvaluate the effect of digital self-learned educational intervention about COVID-19 using the protection motivation theory (PMT) on non-health students' knowledge and self-protective behaviors at Saudi Electronic University (SEU).
methodsA quasi-experimental study was accomplished at three randomly chosen branches of SEU (Riyadh, Dammam, Jeddah) using a multistage sampling technique to conveniently select 219 students. An electronic self-administered questionnaire was used, which included three scales for assessing the students' knowledge, self-protective behaviors, and the constructs of the PMT. The educational intervention was designed using four stages: need assessment, planning, implementation, and evaluation. A peer-reviewed digital educational content was developed after assessing the participants' educational needs using the pretest. Then, distributed through their university emails. A weekly synchronous Zoom cloud meeting and daily key health messages were shared with them. Finally, the post-test was conducted after two months.
resultsThe mean participants' age (SD) among the experimental group was 28.94 (6.719), and the control group was 27.80 (7.256), with a high female percentage (63.4%, 73.8%) and a previous history of direct contact with verified COVID-19 patients (78.6%, 69.2%), respectively. A significant positive mean change (
conclusionsThe digital PMT-based self-learned educational intervention effectively boosts non-health university students' COVID-19 knowledge, protection motivation, and self-protective behaviors. Thus, PMT is highly praised as a basis for COVID-19-related educational intervention and, on similar occasions, future outbreaks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.