A Population-Based Intervention for the Prevention of Falls and Fractures in Home Dwelling People 65 Years and Older in South Germany: Protocol
Bibliographic record
Abstract
BACKGROUND: Falls and fall-related injuries pose a major threat to older peoples' health, and are associated with increased morbidity and mortality. In the course of demographic changes, development and implementation of fall prevention strategies have been recognized as an urgent public health challenge. Various risk factors for falls and a number of effective interventions have been recognized. A substantial proportion of falls occur for people who are neither frail nor at high risk. Therefore, population-based approaches reaching the entire older population are needed. OBJECTIVE: The objective of the study presented is the development, implementation, and evaluation of a population-based intervention for the prevention of falls and fall-related injuries in a medium sized city in Germany. METHODS: The study is designed as a population-based approach. The intervention community is a mid sized city named Reutlingen in southern Germany with a population of 112,700 people. All community dwelling inhabitants 65 years and older are addressed. There are two main measures that are defined: (1) increase of overall physical activity, and (2) reduction of modifiable risk factors for falls such as deficits in strength and balance, home and environmental hazards, impaired vision, unsafe footwear, and improper use of assistive devices. The implementation strategies are developed in a participatory community planning process. These might include, for example, training of professionals and volunteers, improved availability of exercise classes, and education and raising awareness via newspaper, radio, or lectures. RESULTS: The study starts in September 2010 and ends in December 2013. It is evaluated primarily by process evaluation as well as by telephone survey. CONCLUSIONS: Physical activity as a key message entails multiple positive effects with benefits on a range of geriatric symptoms. The strength of the design is the development of implementation strategies in a participatory community planning. The problems that we anticipate are the dependency on the stakeholders' willingness to participate, and the difficulty of evaluating population-based programs by hard end points.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".