2.X.1. Sleep Health Disparities across the life-course: from research to public health policy
Bibliographic record
Abstract
Abstract Poor sleep health is a recognized common problem across the life cycle, globally. Factors that contribute to this problem include the demands of a “Western” lifestyle (urgency, productivity), economic stressors, social and environmental determinants of health, and global crises including international conflicts, political tensions, and climate change. There is a growing consensus that optimizing sleep health should be a public health priority because poor sleep patterns are a modifiable risk factor for myriad health outcomes, such as cardiovascular disease, diabetes, cancer, cognitive decline, mental illness, and early mortality. In Canada, a nationwide sleep consortium funded by the Canadian Institutes of Health Research (CIHR) was recently formed with a mandate to generate new knowledge that will inform clinical practice and support public health initiatives to deliver resources where they are most needed. The proposed workshop organized by the Chronic Disease EPH section will start with a general overview of the impact of sleep health disparities across the life-course, from childhood to older age, as well as discuss evidence-based public health approaches to promote sleep hygiene and mitigate the risk of adverse health outcomes, including chronic disease (Saverio Stranges). It will be followed by three presentations to address the root causes of sleep health disparities and their impact on chronic disease (Dayna Johnson); participatory research with families and care providers on infant sleep intervention development (Elizabeth Keys), and strategies to address common sleep disorders among middle-aged and older adults (Tetyana Kendzerska). Key messages • Sleep health disparities are a neglected public health issue. • Optimizing sleep health is both a clinical and public health priority, as sleep patterns represent a modifiable risk factor for a range of adverse health outcomes.
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 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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".