Active Sick Leave for Patients With Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Semistructured interviews, group discussions, and a mailed survey. OBJECTIVE: To identify barriers to the use of active sick leave (ASL) and to design an intervention to improve the use of ASL by patients with low back pain. SUMMARY OF BACKGROUND DATA: ASL was introduced in Norway in 1993 to encourage people on sick leave to return to modified work. With ASL the National Insurance Administration (NIA) pays 100% of wages, thereby allowing the employer to engage a substitute worker at no extra cost, in addition to the worker on ASL. Arranging ASL requires cooperation between the general practitioner (GP), employer, local NIA staff, and the patient, which may explain why ASL was used in less than 1% of the eligible sick leave cases in 1995, despite strong support from all players. METHODS: The authors conducted five in-depth interviews at a workplace where ASL was successfully implemented. Questionnaires were sent to 89 GPs, 102 workplace representatives, and 22 local NIA officers in three counties. Five patients with back pain who had used ASL were interviewed in a focus group, and 10 patients with back pain who had not used ASL were interviewed using a structured guide. Five workplaces participated in a dialogue conference. Data collection and analysis were iterative, and new data were constantly compared with the previously analyzed materials. RESULTS: About 80% of the GPs, employers, and NIA officers believed ASL is effective in reducing long-term sick leave. Among the barriers identified were lack of information, lack of time, and work flow barriers such as poor communication and coordination of activities between the players required to carry out ASL. Two strategies were designed to improve the workflow between them. A passive implementation strategy was designed to require a minimum amount of economic and administrative support. It included targeted information, clinical guidelines for low back pain, a reminder to GPs in the sick leave form, and a standardized agreement. A proactive strategy included the same four elements plus a kick-off continuing education seminar for GPs and a trained resource person to facilitate the use of ASL. CONCLUSIONS: Having all the players onside may be essential, but it is not sufficient to bring about action in workplace strategies for patients with low back pain. If early return to modified work is effective, implementing it may require interventions targeted at identified barriers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".