Frequency and type of chronic pain care approaches used for elderly residents in Japan and the factors influencing these approaches
Bibliographic record
Abstract
AIM: To assess the frequency at which various chronic pain care (CPC) approaches were used while managing older residents of the Health Service Facilities for the Elderly Requiring Care (HSFERC) in Japan and to assess the factors related to nurses and care workers that influence this care. METHODS: A descriptive study design was used. The population comprised 31 nurses, 92 care workers, and 18 residents with chronic pain in eight HSFERC centers located in three provincial cities in Japan. A questionnaire was formulated by using the data collected by a literature review to assess the frequencies at which various CPC approaches were applied and the factors that might influence this care. RESULTS: The most frequently preferred CPC approaches were gentle handling and support while providing daily care, listening attentively, and providing a recreational activity. The factors that affected the provision of CPC were the qualifications, years of experience of aged care, and experience of studying about chronic pain. The nurses tended to have a misconception regarding the manner in which the residents complained of pain and their pain sensitivity. Furthermore, organizational strategies for pain management were not reported by the nurses and care workers. CONCLUSIONS: In order to provide effective and active CPC, ongoing education about pain and cooperation between nurses and care workers to manage residents' pain are highly recommended.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".