Geriatrician-led evidence-based Falls Prevention Clinic: a prospective 12-month feasibility and acceptability cohort study among older adults
Bibliographic record
Abstract
OBJECTIVE: We assessed the feasibility and acceptability of delivering a geriatrician-led evidence-based Falls Prevention Clinic to older adults with a history of falls. DESIGN: 12-month prospective cohort study. SETTING: Vancouver Falls Prevention Clinic, Vancouver, British Columbia, Canada (www.fallsclinic.ca). PARTICIPANTS: 188 community-dwelling older adults aged ≥70 years who received a baseline assessment at the Vancouver Falls Prevention Clinic due to having had at least one fall resulting in medical attention in the previous 12 months. Fifty-six per cent of participants were also participating in a randomised controlled trial. MEASUREMENTS: Feasibility was ascertained by measuring demand (clinic attendance). Acceptability was measured by compliance with recommendations, completion of monthly fall calendars and patient experience. RESULTS: The attendance was 65% of those eligible and invited. This indicates feasibility for demand. 155 received at least one of the following clinical management recommendations from four domains (compliance reported in %): (1) medication changes (78%); (2) exercise prescription (58%); (3) referrals to other healthcare professionals (78%); and/or (4) lifestyle modifications (35%) excluding exercise. Overall compliance to all recommendations was 69%. Patient experience was related to factors impacting patient perceived physical benefit and attributes influencing patient satisfaction. CONCLUSION: This study demonstrated the feasibility and acceptability of a multifactorial intervention approach based on best available evidence-based medicine.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".