[Public health nurses' activities after the initiation of long-term care insurance (Part 1). Focus on operations for long-term care insurance].
Bibliographic record
Abstract
OBJECTIVE: We conducted a nationwide questionnaire survey to elucidate the present status of public health nurses (PHNs) after initiation of the Long-Term Care Insurance (LTCI) system, in order to study the influence of the allocation of PHNs upon the performance of the system. METHODS: In March 2001, a questionnaire was mailed to 1,344 municipalities throughout Japan (all cities and 23 wards in Tokyo; one quarter of towns/villages). RESULTS: Responses to the questionnaire were received from 569 (42.3%). The largest percentage (36.4%) of PHNs was assigned to Elderly Health, 29.8% was assigned to Maternal and Child Health, and 10.2% assigned to the LTCI. Among LTCI operations, follow-up of individuals not certified as requiring care, guidance for the family caregiver, and counseling/grievance procedures were performed with participation of PHNs in more than 80% of municipalities. In urban areas, PHNs participated in training/guidance/advice for care certification investigators/care managers, guidance for service providers, and preparation/coordination for the Certification Committee for Long-Term Care Need in 60-80% municipalities, while those who participated in these activities were only 30-60% in towns/villages. The participation ratios were highest in municipalities where PHNs were allocated to the LTCI department for almost all of the activities except individual counseling. More PHNs from LTCI departments participated in certificating of care need and improving quality of care plans/services than from other departments in offices of municipalities. On the other hand, more PHNs in Welfare or Public Health-Welfare sections than in the LTCI were involved in individual counseling, guidance for both clients and caregivers, or in case management. Training or guidance for service providers was conducted in nearly 50% of municipalities and the performance rates were lower in towns/villages. In open-ended items of the questionnaire, the following problems were mentioned: difficulties in collaboration between sections; large workloads for certification of care need/care management, and difficulties in capturing information about clients. CONCLUSION: The allocation of PHN influences the activities of the LTCI system, especially in relation to quality assurance. Recently, the sections to which PHNs are posted have expanded to include LTCI and Welfare. While improvement in the quality of LTCI services is anticipated, efforts are needed to strengthen collaboration among different departments. In towns/villages, performance rates of activities such as guidance for service providers of LTCI are low, suggesting the support from prefectural health centers in necessary.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".