Trends in high‐impact papers in nursing research published from 2008 to 2018: A web of science‐based bibliometric analysis
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
OBJECTIVE: To assess the overall trends in the development and citation impact of high-impact papers in nursing research worldwide to gain insight into the focus areas of nursing research. BACKGROUND: Bibliometric method is proved to be effective in analysing the papers' characteristics, and it gained considerable interest from the scientific community in recent years. An analysis of the characteristics and intrinsic patterns of high-impact papers in nursing research will provide an objective reflection of the research hot spots. Nursing managers can pointedly increase funding amount and strengthen research cooperation in order to put the scientific results into management practice. METHODS: Bibliometric methods and visualization software were used to comprehensively analyse high-impact papers in nursing research in terms of development trends, countries/regions, distribution of subject areas, research institutes, collaborative networks and subject terms. RESULTS: There were 6,886 papers between 2008 and 2018. The number of papers increased from 528 in 2008 to 723 in 2015, and then remained above 600 in 2016 and 2017. These papers were mainly distributed in nursing, oncology, paediatrics, gynaecology, teaching and education, and cardiac and cardiovascular systems and were cited by 128,845 papers that came from 89 Web of Science subject areas. Papers in nursing research accounted for the largest share of these citations. The top five countries in the world in terms of the number of high-impact papers were the United States, Australia, the United Kingdom, Canada and Sweden. The research institutions with the highest number of high-impact papers worldwide were the University of California System, the University of Pennsylvania, the University of North Carolina, the University of London and the University of Technology Sydney. In this data set, it was shown that research collaborative circles have been formed in the United States, Australia, Canada and Europe; the subject-term analysis indicated that 'women' and 'students' have always been high-interest populations for high-impact papers and that cancer is still one of the greatest threats to human health. Furthermore, the subject terms of high-impact papers in nursing research have gradually evolved from 'disease' and 'therapy' to 'symptoms'. CONCLUSION: In recent years, the number of high-impact papers published each year in nursing research has grown over time. Nursing has been shown to be a highly specialized subject, and the majority of its high-impact papers have been published by research institutions. Although cross-regional collaborations are beginning to emerge, there is much room for improvement in this regard. Finally, women, students, cancer and symptomatic care are the current focus areas in nursing research. IMPLICATIONS FOR NURSING MANAGEMENT: This study informs nursing managers within the nursing research field about subject areas, collaborative networks and hot topics. It is beneficial to pay attention to studies, manage scientific outputs, allocate resources, seek cooperation and improve the work efficiency of scientific research management.
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Étiquettes directes de modèles (non validées)
Étiquettes de catégorie et de devis d'étude par modèle, issues des rondes d'étiquetage. C'est une sortie machine, non validée, et le désaccord entre modèles est livré comme donnée. Aucun devis ici n'est encore validé contre MEDLINE.
| Bras | Catégories | Devis d'étude | Confiance |
|---|---|---|---|
| gemma | Bibliométrie Domaine: non disponible · Genre: Empirique Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: non | Observationnel | low |
| gpt | Bibliométrie Domaine: non disponible · Genre: Empirique Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: non | Autre devis | high |
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,048 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,907 | 0,967 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,004 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 découle