Can the Healthcare Assistant in General Practice provide Preventative Support for Older People to Reduce Risk of Falls?
Bibliographic record
Abstract
In 2014, 2,734 people over 65 across the four London boroughs were predicted to have been admitted to a hospital because of falls. This figure is predicted to rise further to 3,766 by 2030. The number of falls can be reduced by up to 30% through development of a multi-agency falls pathway focussing on early identification and prevention, and multi-factorial assessment and intervention for people at high risk of falling (1). The role of the health-care assistant (HCA) has developed rapidly in general practice and HCAs can make an increasingly useful contribution to the skill mix in general practice. The project aimed to evaluate; the suitability of HCAs delivering GaitSmart assessments in the GP clinical pathway, GaitSmart protocol for detecting gait deficiency and improving gait and patient reported outcomes (Gait Speed, GaitSmart Score, Falls Efficacy Scale Questionnaire (FES-I) and Edmonton Frailty Score (EFS). The programme produced positive patient outcomes, thus reducing the burden on physiotherapists. Participants who completed the three-session GaitSmart intervention programme showed an average improvement in their gait characteristics
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".