Psychosocial Treatment Techniques to Augment the Impact of Physiotherapy Interventions for Low Back Pain
Bibliographic record
Abstract
PURPOSE: The present study examined the profile of physical and psychosocial changes that occur in physiotherapy intervention when patients also participate in a psychosocial intervention. The psychosocial intervention, delivered by physiotherapists, was designed to target catastrophic thinking, fear of pain, perceived disability, and depression. METHODS: The study sample consisted of 48 individuals referred for the rehabilitation treatment of disabling back pain. Half the sample was enrolled in a physiotherapy intervention only; the other half was enrolled in a psychosocial intervention in addition to receiving a physiotherapy intervention. RESULTS: At post-treatment, the two treatment groups did not differ significantly on measures of pain severity, physical function, or self-reported disability. Patients who participated in the psychosocial intervention in addition to physiotherapy showed significantly greater reductions in pain catastrophizing, fear of movement, and depression than patients who received only the physiotherapy intervention. Reductions in psychosocial risk factors contributed to reduced use of the health care system, reduced use of pain medication, and improved return-to-work outcomes. CONCLUSIONS: The findings of the present study suggest that a psychosocial intervention provided by physiotherapists can lead to meaningful reductions in psychosocial risk factors for pain and disability and may contribute to more positive rehabilitation outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".