HEALTHCARE AIDE-FOCUSED INTERVENTIONS TO IMPROVE PAIN MANAGEMENT IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Pain is endemic for residents of long-term care homes, with many residents experiencing pain daily. Given that healthcare aides provide most daily care for residents, they are ideally situated to deliver timely assessment and non-drug interventions for managing resident pain. In this Cochrane-style systematic review, we searched 7 databases to identify intervention studies that included long-term care residents aged ≥60 years who received interventions to reduce chronic pain. Interventions were either delivered by healthcare aides at the resident level or were directed at healthcare aides to improve their pain management practices. We screened 400 titles/abstracts and 152 full-text articles. Nine studies met inclusion criteria and were included in a narrative review. Due to the limited number of studies and variety of study designs, data were insufficient to perform meta-analyses or thematic analysis. Three studies described pain interventions delivered by healthcare aides at the resident level reporting significant improvement of pain. Six studies described pain interventions delivered to healthcare aides. Results of these interventions were inconsistent; 2 reported significant improvements in pain-related outcomes (e.g., resident pain, monitoring of pain), 3 reported insignificant changes, and 1 reported a positive correlation between measured pain and pain medication use. We concluded that despite the paucity of research in this area, this systematic review provides preliminary support for pain interventions by healthcare aides for long-term care residents. Future research exploring interventions for healthcare aides to take greater roles in pain management could unlock further improvements in resident care.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".