Physical Therapists’ Use of Interventions With High Evidence of Effectiveness in the Management of a Hypothetical Typical Patient With Acute Low Back Pain
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Evidence-based practice aims to improve patient care and service delivery, particularly in the management of individuals with low back pain (LBP), the largest client group seen by outpatient physical therapists. The purpose of this study was to determine the prevalence of use of interventions with evidence of effectiveness in the management of acute nonspecific LBP by physical therapists. SUBJECTS: A multicenter cross-sectional study was conducted on 100 physical therapists working with patients with LBP. METHODS: Using a telephone-administered interview, therapists described their current and desired treatment practices for a typical case of LBP. Each intervention reported was coded according to its evidence of effectiveness (strong, moderate, limited, or none). Information on clinician, workplace, and client characteristics also was obtained. RESULTS: The prevalence of use of interventions with strong or moderate evidence of effectiveness was 68%. However, 90% to 96% of therapists also used interventions for which research evidence was limited or absent. Users of interventions with high evidence of effectiveness, as compared with nonusers, had graduated more recently and had taken a higher number of postgraduate clinical courses. DISCUSSION AND CONCLUSION: Although most therapists use interventions with high evidence of effectiveness, much of their patient time is spent on interventions that are not well reported in the literature. The results indicate the need for improvement in the quality of clinical research as well as its dissemination and implementation in a way that is appealing to therapists, such as through practice-related courses.
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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 | 0.012 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".