A mixed-methods study on the pharmacological management of pain in Australian and Japanese nursing homes
Bibliographic record
Abstract
BACKGROUND: Understanding how analgesics are used in different countries can inform initiatives to improve the pharmacological management of pain in nursing homes. AIMS: To compare patterns of analgesic use among Australian and Japanese nursing home residents; and explore Australian and Japanese healthcare professionals' perspectives on analgesic use. METHODS: Part one involved a cross-sectional comparison among residents from 12 nursing homes in South Australia (N = 550) in 2019 and four nursing homes in Tokyo (N = 333) in 2020. Part two involved three focus groups with Australian and Japanese healthcare professionals (N = 16) in 2023. Qualitative data were deductively content analysed using the World Health Organization six-step Guide to Good Prescribing. RESULTS: Australian and Japanese residents were similar in age (median: 89 vs 87) and sex (female: 73% vs 73%). Overall, 74% of Australian and 11% of Japanese residents used regular oral acetaminophen, non-steroidal anti-inflammatory drugs or opioids. Australian and Japanese healthcare professionals described individualising pain management and the first-line use of acetaminophen. Australian participants described their therapeutic goal was to alleviate pain and reported analgesics were often prescribed on a regular basis. Japanese participants described their therapeutic goal was to minimise impacts of pain on daily activities and reported analgesics were often prescribed for short-term durations, corresponding to episodes of pain. Japanese participants described regulations that limit opioid use for non-cancer pain in nursing homes. CONCLUSION: Analgesic use is more prevalent in Australian than Japanese nursing homes. Differences in therapeutic goals, culture, analgesic regulations and treatment durations may contribute to this apparent difference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".