A systematic review on self-management education campaigns for back pain
Bibliographic record
Abstract
BACKGROUND: Evidence-based clinical practice guidelines on back pain recommend early management and use of approaches that emphasise self-management, psychological and physical therapies. Lately, mass media campaigns, addressing misconceptions about back pain, have been conducted in developed countries. OBJECTIVES: This study retrieved and synthesised the contents of back pain messages and described the outcomes and effectiveness of the media campaigns. METHOD: Seventeen key words and 10 electronic databases were used to conduct a search between February and July 2018. Authors screened titles, abstracts and full-text articles independently to identify eligible studies. Data were reported using narratives because of heterogeneity in the outcomes. RESULTS: Appraisal of articles was done using the Physiotherapy Evidence Database scale for randomised controlled trials (RCT) (one) or the Joanna Briggs Institute checklist for non-RCT (four). The campaigns were conducted in the general population in Australia, Canada, Norway, the Netherlands and Scotland. The message 'stay as active as possible' increased participants' awareness and influenced their health beliefs and healthcare utilisation behaviours resulting in reductions in sick leave days, work disability, healthcare utilisation and claims. CONCLUSION: The back pain campaign message 'stay as active as possible' increased participants' awareness and influenced their health beliefs and healthcare utilisation behaviours. Even though the campaigns were done in high-income countries, their contents and methods are transferable to developing countries. However, their implementation must be tailored and efficient and cost-effective methods need to be explored. CLINICAL IMPLICATIONS: Providing information on back pain can contribute to significant changes in sickness behaviours and beliefs.
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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".