Statement on national worklife priorities
Bibliographic record
Abstract
The National Institute for Occupational Safety and Health (NIOSH) WorkLife Initiative (WLI) [http://www.cdc.gov/niosh/worklife] seeks to promote workplace programs, policies, and practices that result in healthier, more productive employees through a focus simultaneously on disease prevention, health promotion, and accommodations to age, family, and life stage. The Initiative incorporates the Institute's foundational commitment to workplaces free of recognized hazards into broader consideration of the factors that affect worker health and wellbeing. Workplace hazards, such as physical demands, chemical exposures, and work organization, often interact with non-work factors such as family demands and health behaviors to increase health and safety risks. New workplace interventions being tested by the first three NIOSH WLI Centers of WorkLife Excellence are exploring innovative models for employee health programs to reduce the human, social, and economic costs of compromised health and quality of life. Many parties in industry, labor, and government share the goals of improving employee health while controlling health care costs. NIOSH convened a workshop in 2008 with representatives of the three Centers of Excellence to develop a comprehensive, long-range strategy for advancing the WorkLife Initiative. The recommendations below fall into three areas: practice, research, and policy. Responding to these recommendations would permit the WorkLife Center system to establish a new infrastructure for workplace prevention programs by compiling and disseminating the innovative practices being developed and tested at the Centers, and elsewhere. The WLI would also extend the customary scope of NIOSH by engaging with multiple NIH Institutes that are already generating research-to-practice programs involving the working-age population, in areas such as chronic disease prevention and management. Research to Practice (r2p) is a concept focused on the translation of research findings, technologies, and information into evidence-based prevention practices and products that are adopted in the workplace or other "real-world" settings. NIOSH's goal is to overcome the translational issues that now prevent state-of-the-art occupational health, health promotion, and chronic disease research findings from benefiting working age populations immediately, regardless of workplace size, work sector, or region of the country.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".