How to Strengthen Non-Motorised Mobility of Elderly People? An Evidence-based Manual for the \nSet-up of Fall Prevention Programmes in Communities
Bibliographic record
Abstract
In the course of life, mobility behaviour and needs change and have to be adapted. With growing age, muscle \nmass reduces continuously. If this natural degradation process is not countered, the risk of falls and getting \ninjured increases. Once a person has experienced a fall, the fear of falling again is likely to evolve. As a \nconsequence, physical activity is associated with feelings of insecurity and is therefore avoided (post-fallsyndrome). \nWithin the age group 55 years and older, almost a quarter of occurring falls in Austria happen in \ntraffic (KFV, 2016). Thus, motivity and health are key prerequisites for a safe, independent and injury-free \nmobility. In order to tackle this topic, the Austrian Road Safety Board (KFV) developed the project “Pimp \nyour Skills”1 (Eichhorn et al., 2016), which focused on strengthening non-motorised mobility of elderly \npeople and, particularly, on fall prevention. As a result, a manual on setting up an effective fall prevention \nprogramme for adults is now available.#
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".