What are physical therapists doing to prevent falls in older adults in Brazil? - Findings from a nationwide survey
Bibliographic record
Abstract
BACKGROUND: Understanding how physical therapists (PTs) approach fall prevention in older adults and factors that may influence their clinical practices is essential for designing knowledge translation strategies. OBJECTIVES: To describe PTs' clinical practices and barriers to implementing fall prevention best practices in older adults and to identify professional characteristics associated with implementation of fall prevention best practices. METHODS: A cross-sectional online survey was conducted. Registered PTs providing care to older adults were recruited through social media platforms. A pre-tested questionnaire assessed clinical practice patterns, sociodemographic and professional characteristics, and behavioral factors influencing the implementation of fall prevention best practices. Data were analyzed descriptively, and multinomial regression identified associations between PTs' characteristics and practice frequency. The Theoretical Domains Framework and the Capability, Opportunity, Motivation-Behaviour model guided questionnaire design and interpretation of findings. RESULTS: Among 454 PTs surveyed, over 65 % reported frequently (often or always) asking patients about falls, identifying and documenting fall risk factors, and implementing fall prevention interventions. Recommended practices such as balance and strength training were commonly implemented. Barriers to fall prevention best practices included patient denial of risk, reluctance to report falls, and adherence challenges. PTs not practicing in geriatrics or those lacking up-to-date fall prevention knowledge were less likely to report consistent use of best practices. CONCLUSION: Brazilian PTs frequently integrate fall prevention into older adult care but face patient-related barriers. Addressing the identified barriers through behavior change strategies could enhance the implementation of fall prevention best practices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".