Insufficient Sleep Syndrome: Is it time to classify it as a major noncommunicable disease?
Bibliographic record
Abstract
Over the last three to four decades, it has been observed that the average total number of hours of sleep obtained per night by normal individuals have decreased. Concomitantly, global figures indicate that insufficient sleep is associated with serious adverse health and social outcomes. Moreover, insufficient sleep has been linked to seven of the fifteen leading causes of death. Additionally, current evidence suggests that sleep plays a significant role in determining cognitive performance and workplace productivity. There is a great need for a systematic analysis of the economic impact of insufficient sleep, particularly given current evidence that this phenomenon, as well as the poor sleep hygiene practices which produce it, is increasing worldwide. This paper takes the view that health authorities around the world need to raise the general awareness of benefits of sleep. There is considerable scope for research into both the public health impact as well as the macroeconomic consequences of insufficient sleep syndrome (ISS). Additionally, various models which estimate the undiagnosed burden of ISS on the GDP (gross domestic product) are needed to prioritize health issues and to highlight the national policies that are necessary to combat this medical problem. Sleep insufficiency has been declared to be a 'public health epidemic'; therefore, we propose ISS as a potential noncommunicable disease. This review elaborates on this topic further, exploring the causes and consequences of insufficient sleep, and thus providing a perspective on the policies that are needed as well as the research that will be required to support and justify these policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".