Bibliographic record
Abstract
STUDY DESIGN: Cluster randomized controlled trial. OBJECTIVE: To evaluate the effectiveness of two strategies to improve the use of active sick leave (ASL) for patients with low back pain. SUMMARY OF BACKGROUND DATA: ASL is a public sickness benefit scheme offered to promote early return to modified work for temporarily disabled workers. It was poorly used, and the authors designed two community interventions to strengthen the implementation of ASL based on the results of a study of barriers to use among back pain patients, employers, general practitioners (GPs), and local National Insurance Administration staff. METHODS: Sixty-five municipalities in three counties in Norway, randomly assigned to a passive intervention, a proactive intervention, or a control group. The interventions were targeted at patients on sick leave for low back pain for more than 16 days (n = 6176), their GPs, employers, and local insurance officers. The passive intervention included reminders about ASL on the sick leave form that GPs must complete, a standard agreement to facilitate ASL, targeted information, and a desktop summary for GPs of clinical practice guidelines for low back pain, emphasizing the importance of advice to stay active. The proactive intervention included these elements plus a resource person to facilitate the use of ASL and a continuing education workshop for GPs. The main outcome measure reported here is the proportion of eligible patients that used ASL. RESULTS: ASL was used significantly more in the proactive intervention municipalities (17.7%) compared with the passive intervention and control municipalities (11.5%, P = 0.018). CONCLUSIONS: A passive intervention that addressed identified barriers to the use of ASL did not increase its use. Although modest, a proactive intervention did increase its use. The main impact of the intervention was through direct contact and motivating telephone calls to patients. To the extent that GPs' practice was changed, it was either patient mediated or by patients bypassing their GP.
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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 | 0.148 | 0.197 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.043 | 0.020 |
| Insufficient payload (model declined to judge) | 0.117 | 0.015 |
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".