New Web-Based System for Recording Public Health Nursing Practices and Determining Best Practices: Protocol of an Exploratory Sequential Design
Bibliographic record
Abstract
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.
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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 | 0.090 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".