Applying the RE-AIM implementation framework to evaluate fall prevention interventions in community dwelling adults with cognitive impairment: a review and secondary analysis
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment (CI) is a risk factor for falls due to environmental or living settings, balance, gait and vision impairments, as well as medications. While previous systematic reviews have focused on the effectiveness of fall prevention programs in adults with cognitive impairment, very limited information is available on their implementation. This review examines what aspects of fall prevention interventions for community-dwelling adults with CI have been reported using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to support successful implementation. METHODS: We examined the included studies from our systematic review, which searched 7 databases for primary and secondary fall prevention interventions involving community-dwelling adults ≥50 years with mild to moderate CI. Reviewers screened citations and extracted data for study characteristics and the 5 dimensions (62 criteria) of the RE-AIM framework. RESULTS: Twelve randomized or clinical controlled trials (RCTs/CCTs) consisting of 8 exercise interventions, 3 multifactorial interventions, and 1 medication treatment were included in the review. Only 4 of 62 criteria were reported by all 12 included studies and 29 criteria were not reported by any of the studies. Five of the included studies reported on 20 or more of the 62 possible RE-AIM criteria and 3 of these studies self-identified as "feasibility" studies. While Reach was the best-reported construct by the included studies, followed by Effectiveness and Implementation, the criteria within the Adoption and Maintenance constructs were rarely mentioned by these studies. In general, there was also wide variation in how each of the criteria were reported on by study authors. CONCLUSION: Based on the reporting of RE-AIM components in this review, we are unable to make connections to successful intervention components and thus practice-based recommendations for fall prevention in those with CI. The lack of detail regarding implementation approaches greatly limits the interpretation and comparisons across studies to fully inform future research efforts.
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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 | 0.079 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".