Transitioning Towards a Virtual Falls Prevention Program for Frail Seniors: Learning from the Experiences of Older Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The literature to date is unable to clearly characterize the appropriateness of virtual care for falls prevention services from the patient perspective. In response to COVID-19, the Falls Prevention Program (FPP) at Sunnybrook Health Sciences Centre was modified to include virtual components. We set out to uncover the experiences of this unique older-adult patient population to inform FPP quality improvement and appropriate incorporation of technology post-pandemic. Methods: FPP patients during the COVID-19 pandemic (February 2020 - February 2022) and their primary caregivers met inclusion criteria. Out of 18 eligible patients, 10 consented to participate in 20-minute, semi-structured telephone interviews conducted and transcribed by the first author. Inductive coding followed by theme generation occurred through collaborative analysis. Results: The participants (n=10) were 60% female, mean age 84 years (SD 5.8), 60% living alone, and 70% university educated. We generated three main themes: 1) First Steps First, revealed a common desire for physical and mental support and the perceived essentials of a successful FPP highlighting the importance of program length and individualized attention; 2) Overcoming Obstacles, highlighted participants' experiences overcoming barriers with technology in the context of an isolating pandemic; and 3) Advancing Care Post-Pandemic, elaborated on the appropriateness of virtual care and delved into the importance of program personalization. Conclusion: The interviewed older adults revealed agreement on the FPP's necessity and the importance of increasing program length, one-on-one interaction, and program flexibility for unique patient needs. Incorporating virtual assessment prior to in-person exercises was largely favoured and should be considered as an appropriate use of technology post-pandemic.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".