<p>Barriers and Facilitators to Older Adults Participating in Fall-Prevention Strategies After Transitioning Home from Acute Hospitalization: A Scoping Review</p>
Bibliographic record
Abstract
PURPOSE: Approximately, 14% of older adults aged 65 years and over experience a fall within 1 month post-hospital discharge. Adequate self-management may minimize the impact of these falls; however, research is lacking on why some older adults engage in self-management to prevent falls while others do not. METHODS: We conducted a scoping review to identify barriers and facilitators to older adults participating in fall-prevention strategies after transitioning home from acute hospitalization. Eligibility criteria were peer-reviewed journal articles published during 2009-2019 which were written in English and contained any of the following keywords or their synonyms: "fall-prevention," "older adults," "post-discharge" and "transition care." We systematically and selectively summarized the findings of these articles using the Joanna Briggs Institute guidelines and the PRISMA-ScR reporting guidelines. Seven bibliographic databases were searched: PubMed/MEDLINE, ERIC, CINAHL, Cochrane Library, Scopus, PsycINFO, and Web of Science. We used the Capability-Opportunity-Motivation-Behavior (COM-B) model of health behavior change as a framework to guide the content, thematic analysis, and descriptive results. RESULTS: Seventeen articles were finally selected. The most frequently mentioned barriers and facilitators for each COM-B dimension differed. Motivation factors include such as older adults lacking inner drive and self-denial of being at risk for falls (barriers) and following-up with older adults and correcting inaccurate perceptions of falls and fall-prevention strategies (facilitators). CONCLUSION: This scoping review revealed gaps and future research areas in fall prevention relative to behavioral changes. These findings may enable tailoring feasible fall-prevention interventions for older adults after transitioning home from acute hospitalization.
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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 | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".