Long Sleepers Sleep More and Short Sleepers Sleep Less: A Comparison of Older Adults Who Sleep Well
Bibliographic record
Abstract
To determine some of the risks and benefits of being a long or short sleeper, psychological adjustment, lifestyle, and sleep parameters were investigated in 239 older adults. Responses of people who slept well and who were either long or short sleepers were studied on 48 variables investigating sleep parameters and sleep-related affect and beliefs; daytime fatigue and sleepiness; demographic factors, including age, sex, and income satisfaction; sleep lifestyle factors, including naps, bedtimes, arising times, and the regularity of these; general lifestyle factors, including regularity of mealtimes, overall daytime pleasantness, perceived busyness, diversity and valence of daily activities, and potentially stressful major life events. In addition, 14 variables evaluated aspects of psychological adjustment, including cognitive and somatic arousal, nocturnal tension, anxious, negative, unpleasant and worrying self-talk, depression, anxiety, overall psychopathology, neuroticism, and life satisfaction. Overall, the results indicate that short sleepers get up earlier, spend less time in bed, and have lower sleep efficiencies than their long sleeper counterparts. They eat breakfast earlier, and of course, they sleep less. Only one of the 14 psychological adjustment variables was significant. In view of the many differences between short and long sleepers described in prior research, the lack of differences observed between long and short sleepers is noteworthy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".