An Interdisciplinary Outpatient Clinic Designed to Reduce Falls in Community-Living Seniors: A Pragmatic Pilot Study
Bibliographic record
Abstract
Background: Tinetti demonstrated that falls among community-dwelling older adults can be prevented through a stepwise, individualized approach. However, in Canada, many current fall prevention clinics implement universal rather than customized interventions. This study evaluated the effectiveness of this original clinic approach on fall risk. Methods: This was a pre-post single-group longitudinal study. At baseline, professionals, including nurses, physiotherapists, and geriatricians, conducted discipline-specific assessments. The team identified specific fall risk factors and proposed personalized, evidence-based interventions. A nurse conducted a structured follow-up to collect data on falls and the patient’s condition over six months, and a physiotherapist performed a final mobility assessment. Results: Fifty-nine older patients referred to the Fall Prevention Clinic completed the study. The fall rate at six months dropped to 36% from 91% in the previous year (p < 0.001), suggesting two-thirds stopped falling. The most common risk factor was deconditioning (46%). No significant differences in risk factors or interventions were found between participants who fell and those who did not after the intervention, except referrals to community occupational therapy, which were more common among those who fell (p = 0.038). The key intervention, Exercise, was prescribed to 85% of the participants. Mobility assessments revealed no significant changes, except for an improvement in lower limb strength (p = 0.02). Conclusions: Fall prevention requires precise identification of risk factors and prompt initiation of targeted interventions. Our model of an interprofessional Fall Prevention Clinic offers a comprehensive approach to identify key modifiable risks and reduce fall incidence in high-risk populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".