Non-pharmacologic and pharmacologic treatments for anxiety in long-term care: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: older adults living in long-term care (LTC) commonly suffer from anxiety symptoms and disorders. We completed a systematic review and meta-analysis to identify efficacious treatments for anxiety symptoms for older adults living in LTC. METHODS: we searched five electronic databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials) to identify treatments for anxiety that have been trialled in LTC. Included studies had to be randomised trials, include residents of LTC, and measure anxiety symptoms as an outcome. RESULTS: the electronic search returned 6,617 articles, 519 were reviewed in full text, and 80 were included in the descriptive synthesis. Limited studies were meta-analysed (n = 10) due to differences in described treatment and comparator conditions. Limited clinically relevant evidence supporting the use of pharmacologic treatments for symptoms of anxiety in LTC was identified. Of the treatments trialled, music compared with usual care (standardised mean difference, SMD: -0.82; 95% confidence interval (CI): -1.31, -0.34), music compared with social interaction (SMD: -0.41; 95% CI: -0.72, -0.10) and massage compared with usual care (SMD: -4.32; 95% CI: -7.44, -1.19) were found to improve anxiety symptoms, however, significant heterogeneity was detected in two comparisons. CONCLUSIONS: a range of non-pharmacologic treatments that improved anxiety symptoms were identified for use in LTC. Although limited evidence exists to support the use of particular treatments, most non-pharmacologic treatments were low-risk interventions that may be readily implemented. Further research is required to assess the treatment effect on residents of LTC with anxiety disorders or clinically relevant symptoms at baseline.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".