Physicians’ Determinants for Sick-listing LBP Patients
Bibliographic record
Abstract
STUDY DESIGN: A systematic review of the literature. OBJECTIVES: Sick-listing is a complex process that involves stakeholders at several levels. Although the physicians are the ones who issue a sick note, little is known about the mechanisms and determinants they use in making a decision about whether to sick-list a patient with low back pain (LBP). The aim of this systematic review is to describe the evidence on determinants used by physicians to sick-list patients with LBP. METHODS: Electronic searches of Medline, EMBASE, PsychInfo, and Cochrane Central were conducted (all years to June 2011). Inclusion criteria included studies of workers with LBP presenting to a physician where sick-listing certification was an outcome of the consultation process. Studies were critically appraised for their internal validity by 2 independent reviewers using a modified version the criteria proposed by Hayden et al. Findings from papers were synthesized into internal and external factors related to the physician. RESULTS: The search identified 1419 unique citations from which 11 papers met the inclusion criteria. The evidence suggests that 2 internal factors are important determinants of sick listing: physicians' personal fear avoidance and distress regarding the complexity of LBP. External factors included patients' expectations, the presence of clinical findings, and the support and general attitude demonstrated by a patients' employer and the availability of modified work. CONCLUSIONS: The current review suggests that physicians need to improve their knowledge regarding options for modified work in the workplace, and about the management of LBP in general. The otherwise beneficial patient-physician relationship and physicians' care for their patients may be an obstacle to following guidelines on LBP management in the sick-listing process. Future studies should address these issues.
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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 | 0.019 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".