Effect of an online digital educational intervention on knowledge and perception of nursing students in Nigeria towards research: a pre- and post-test study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».