New Web-Based System for Recording Public Health Nursing Practices and Determining Best Practices: Protocol of an Exploratory Sequential Design
Notice bibliographique
Résumé
BACKGROUND: Digitalization and information and communication technology (ICT) promote effective, efficient individual and community care. Clinical terminology or taxonomy and its framework visualize individual patients' and nursing interventions' classifications to improve their outcomes and care quality. Public health nurses (PHNs) provide lifelong individual care and community-based activities while developing projects to promote community health. The linkage between these practices and clinical assessment remains tacit. Owing to Japan's lagging digitalization, supervisory PHNs face difficulties in monitoring each department's activities and staff members' performances and competencies. Randomly selected prefectural or municipal PHNs collect data on daily activities and required hours every 3 years. No study has adopted these data for public health nursing care management. PHNs need ICTs to manage their work and improve care quality; it may help identify health needs and suggest best public health nursing practices. OBJECTIVE: We aim to develop and validate an electronic recording and management system for evaluating different public health nursing practice needs, including individual care, community-based activities, and project development, and for determining their best practices. METHODS: We used a 2-phase exploratory sequential design (in Japan) comprising 2 phases. In phase 1, we developed the system's architectural framework and a hypothetical algorithm to determine the need for practice review through a literature review and a panel discussion. We designed a cloud-based practice recording system, including a daily record system and a termly review system. The panels included 3 supervisors who were prior PHNs at the prefectural or municipal government, and 1 was the executive director of the Japanese Nursing Association. The panels agreed that the draft architectural framework and hypothetical algorithm were reasonable. The system was not linked to electronic nursing records to protect patient privacy. Phase 2 validated each item through interviews with supervisory PHNs using a web-based meeting system. A nationwide survey was distributed to supervisory and midcareer PHNs across local governments. RESULTS: This study was funded in March 2022 and approved by all ethics review boards from July to September and November 2022. Data collection was completed in January 2023. Five PHNs participated in the interviews. In the nationwide survey, responses were obtained from 177 local governments of supervisory PHNs and 196 midcareer ones. CONCLUSIONS: This study will reveal PHNs' tacit knowledge about their practices, assess needs for different approaches, and determine best practices. Additionally, this study will promote ICT-based practices in public health nursing. The system will enable PHNs to record their daily activities and share them with their supervisors to reflect on and improve their performance, and the quality of care to promote health equity in community settings. The system will support supervisory PHNs in creating performance benchmarks for their staff and departments to promote evidence-based human resource development and management. TRIAL REGISTRATION: UMIN-ICDR UMIN000049411; https://tinyurl.com/yfvxscfm. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45342.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,090 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,003 |
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 ».