Disability prevention and communication among workers, physicians, employers, and insurers—current models and opportunities for improvement
Bibliographic record
Abstract
PURPOSE: To review prevailing models of disability management and prevention with respect to communication, and to suggest alternative approaches. METHOD: Review of selected articles. RESULTS: Effective disability management and return to work strategies have been the focus of an increasing number of intervention programmes and associated research studies, spanning a variety of worker populations and provider and business perspectives. Although primary and secondary disability prevention approaches have addressed theoretical basis, methods and costs, few identify communication as a key factor influencing disability outcomes. Four prevailing models of disability management and prevention (medical model, physical rehabilitation model, job-match model, and managed care model) are identified. The medical model emphasizes the physician's role to define functional limitations and job restrictions. In the physical rehabilitation model, rehabilitation professionals communicate the importance of exercise and muscle reconditioning for resuming normal work activities. The job-match model relies on the ability of employers to accurately communicate physical job requirements. The managed care model focuses on dissemination of acceptable standards for medical treatment and duration of work absence, and interventions by case managers when these standards are exceeded. Despite contrary evidence for many health impairments, these models share a common assumption that medical disability outcomes are highly predictable and unaffected by either individual or contextual factors. As a result, communication is often authoritative and unidirectional, with workers and employers in a passive role. CONCLUSION: Improvements in communication may be responsible for successes across a variety of new interventions. Communication-based interventions may further improve disability outcomes, reduce adversarial relationships, and prove cost-effective; however, controlled trials are needed.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".