Can physical therapists counsel patients with lifestyle-related health conditions effectively? A systematic review and implications
Bibliographic record
Abstract
OBJECTIVE: Healthcare professionals' competence in health behavior change has lagged behind other clinical competencies despite the well-established relationship between lifestyle behavior and health. We conducted a systematic review to examine whether physical therapists, given their unique practice pattern, can counsel effectively. METHODS: Databases including MEDLINE, EMBASE, CINAHL, PsycINFO, and the Cochrane Database of Systematic Reviews were assessed from 1950 to July 2010. Studies were limited to the English, German, and Dutch languages. The methodological quality was evaluated (Downs and Black tool). RESULTS: Seven source articles with a mean quality score of 16.57 ± 4.24 points (range: low = 0; high = 28) were retrieved. Given considerable methodological heterogeneity, the studies were compared in a narrative synthesis. The target populations, types and periods of interventions, outcome measures, and findings were analyzed. CONCLUSION: Physical therapists can effectively counsel patients with respect to lifestyle behavior change, at least in the short term. They can be effective health counselors individually or within an interprofessional team. PRACTICE IMPLICATIONS: Multiple health behavior change needs to be a primary twenty-first century clinical competence in physical therapy. Future studies will establish the degree to which effective health counseling augments physical therapy as well as health outcomes, in the long as well as short term.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".