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Active Sick Leave for Patients With Back Pain

2002· article· en· W65995551 on OpenAlexaff
Inger B. Scheel, Kåre Birger Hagen, Andrew D Oxman

Bibliographic record

VenueSpine · 2002
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsMinnow Environmental (Canada)
Fundersnot available
KeywordsSick leaveMedicineFamily medicineFocus groupIntervention (counseling)Back painNursingLow back painPhysical therapyAlternative medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.029
GPT teacher head0.341
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2002
Admission routes1
Has abstractyes

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