The prevalence and management of current daily pain among older home care clients ☆
Bibliographic record
Abstract
The aim of this cross-sectional study was to examine the prevalence and correlates of pharmacotherapy for current daily pain in older home care clients, focusing on analgesic type and potential contraindications to treatment. The sample included 2779 clients aged 65+years receiving services from Community Care Access Centres in Ontario during 1999-2001. Clients were assessed with the Resident Assessment Instrument-Home Care (RAI-HC). Prescription and over-the-counter (OTC) medications listed on the RAI-HC were used to categorize analgesic treatment into two groups (relative to no analgesic use): use of non-opioids (acetaminophen or non-steroidal anti-inflammatory drugs only); and, use of opioids alone or in combination with non-opioids. Associations between client characteristics and analgesic treatment among those in current daily pain were examined using multivariable multinomial logistic regression. Approximately 48% (n=1,329) of clients had daily pain and one-fifth (21.6%) of this group received no analgesic. In multivariable analyses, clients aged 75+years and those with congestive heart failure, diabetes, other disease-related contraindications, cognitive impairment and/or requiring an interpreter were significantly less likely to receive an opioid alone or in combination with a non-opioid. Clients with congestive heart failure and without a diagnosis of arthritis were significantly less likely to receive a non-opioid alone. A diagnosis of arthritis or cancer and use of nine or more medications were significantly associated with opioid use. The findings provide evidence of both rational prescribing practices and potential treatment bias in the pharmacotherapeutic management of daily pain in older home care clients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".