Clinical Decision Making in Exercise Prescription for Fall Prevention
Bibliographic record
Abstract
BACKGROUND: Physical therapists often prescribe exercises for fall prevention. Understanding the factors influencing the clinical decision-making processes used by expert physical therapists working in specialist fall and balance clinics may assist other therapists in prescribing exercises for fall prevention with greater efficacy. OBJECTIVES: The objective of this study was to describe the factors influencing the clinical decision-making processes used by expert physical therapists to prescribe exercises for fall prevention. DESIGN: This investigation was a qualitative study from a phenomenological perspective. METHODS: Semistructured telephone interviews were conducted with 24 expert physical therapists recruited primarily from the Victorian Falls Clinic Coalition. Interviews focused on 3 exercise prescription contexts: face-to-face individual therapy, group exercise programs, and home exercise programs. Interviews elicited information about therapist practices and the therapist, patient, and environmental factors influencing the clinical decision-making processes for the selection of exercise setting, type, dosage (intensity, quantity, rest periods, duration, and frequency), and progression. Strategies for promoting adherence and safety were also discussed. Data were analyzed with a framework approach by 3 investigators. RESULTS: Participants described highly individualized exercise prescription approaches tailored to address key findings from physical assessments. Dissonance between prescribing a program that was theoretically correct on the basis of physiological considerations and prescribing one that a client would adhere to was evident. Safety considerations also were highly influential on the exercise type and setting prescribed. Terminology for describing the intensity of balance exercises was vague relative to terminology for describing the intensity of strength exercises. CONCLUSIONS: Physical therapists with expertise in fall prevention adopted an individualized approach to exercise prescription that was based on physical assessment findings rather than "off-the-shelf" exercise programs commonly used in fall prevention research. Training programs for people who prescribe exercises for older adults at risk of falling should encompass these findings.
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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 | 0.048 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".