Use of Mental Health Interventions by Physiotherapists to Treat Individuals with Chronic Conditions: A Systematic Scoping Review
Bibliographic record
Abstract
Purpose: Physiotherapists work with people with chronic conditions and can act as catalysts for behavioural change. Physiotherapy has also seen a shift to a bio-psychosocial model of health management and interdisciplinary care, which is important in the context of chronic conditions. This scoping review addressed the research question “How do physiotherapists use mental health–based interventions in their treatment of individuals with chronic conditions?” Method: The Embase, MEDLINE, PsycINFO, and CINAHL databases were searched, and a variety of study designs were included. Data were categorized and descriptively analyzed. Results: Data were extracted from 103 articles. Low back pain (43; 41.7%) and non-specified pain (16; 15.5%) were the most commonly researched chronic conditions, but other chronic conditions were also represented. Outpatient facilities were the most common setting for intervention (68; 73.1%). A total of 73 (70.9%) of the articles involved cognitive–behavioural therapy, and 41 (40.0%) included graded exercise or graded activity as a mental health intervention. Conclusions: Physiotherapists can use a variety of mental health interventions in the treatment of chronic conditions. More detailed descriptions of treatment and training protocols would be helpful for incorporating these techniques into clinical practice.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".