Self‐management interventions for issues identified in a geriatric assessment: A systematic review
Bibliographic record
Abstract
BACKGROUND: With the development of electronic geriatric assessment (GA), recommendations for self-management can be provided to patients without the presence of health care providers. Our research question was to identify what self-management interventions can be used by patients to address issues identified in GA and to determine their effect on patient-centered outcomes such as quality of life, health, mood, cognition, and functional status. METHODS: Searches were conducted on July 13, 2021, by a health sciences librarian in Medline, Embase, CINAHL, PsycInfo, and the Cochrane Library. A combination of database-specific subject headings and text word searches was used such as self-management, a key word for each of the geriatric assessment domains and older adults. Two independent reviewers reviewed abstracts and full texts for inclusion and abstracted data. Narrative synthesis was used to summarize findings. RESULTS: Among 28,520 abstracts reviewed, 34 randomized controlled trials were included. The most frequently studied geriatric domains were mood (n = 13 studies), mobility/falls (n = 12), quality of life (n = 11), and functional status (n = 7). The majority of studies demonstrated positive effects on mobility/falls (9 of 12), pain (3 of 5), comorbidity (4 of 4), and medication management (4 of 4). Most studies were of low to moderate quality. All geriatric domains were targeted in at least one study. CONCLUSIONS: Low- to moderate-quality studies show a variety of effective self-efficacy-targeted interventions exist for older adults to improve several important geriatric domains and related outcomes. However, long-term effects, validation, and scalability of these interventions remain largely unknown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".