Effect of an online digital educational intervention on knowledge and perception of nursing students in Nigeria towards research: a pre- and post-test study
Bibliographic record
Abstract
Abstract Nursing research is a scientific process crucial for evidence-based practice. Learning about the research process empowers nursing students to comprehend research, actively engage in research endeavours, and integrate findings into clinical practice. Although evidence suggests that educational interventions might improve the research competencies of nursing students, little is known regarding the impact of such interventions on this population, which prompted this study, which aimed to ascertain the effects of an online digital educational intervention on the knowledge and perception of nursing students in Nigeria towards research. The study utilized a pre-test and post-test approach. Nursing students between the second and final year of their studies were included. One hundred fifty nursing students received the educational intervention on June 17, 2023, while One Hundred thirty-five completed the pre- and post-test through an online questionnaire. Descriptive statistics were used to summarise the main features of the data, while hypotheses were tested using the Mann-Whitney U test. The majority of the respondents were females (75.3%), between 20-29 years (84.0%), Yoruba (56.0%), from the South-western part of Nigeria (46.7%), in the third year of training (51.3%), attend a school of Nursing (57.3%), and private-owned institutions (48.0%). We discovered a significant difference between the research knowledge of Nigerian nursing students before and after the intervention (p = <.001), with a medium effect size ( d = 0.55). Similarly, there was a significant difference between the research perception of Nigerian nursing students before and after the intervention, p = .012, with a medium effect size of ( d = 0.24). The online digital educational intervention significantly increased knowledge and positively changed the perception level of Nigerian nursing students toward research. This suggests that similar interventions could greatly benefit Nigerian nursing students in the future, enhancing their research competencies and ultimately improving patient care. Author summary Strong research skills are essential for improving patient care. However, many nursing students in Nigeria find research challenging and see it mainly as an academic requirement rather than a vital part of clinical practice. This inspired us to design and test an online educational program to make research more accessible and engaging for nursing students. In this study, we worked with nursing students across Nigeria, guiding them through an online session on the research process, from choosing a topic to writing and publishing their work. Before and after the session, students completed questionnaires that measured their knowledge and perceptions of research. The results showed a clear improvement: students gained more understanding of research and developed a more positive attitude towards it. These findings suggest that digital learning can help bridge knowledge gaps and prepare future nurses to apply research evidence in patient care. By integrating online learning into nursing education, we can empower students to see research not as a burden, but as a tool for transforming healthcare practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".