The complex association between bedtime screen use and adult sleep health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether bedtime screen use is associated with sleep health and if this association varies by biological sex, age, and income among adults in Canada. METHODS: Data were collected through a national stratified random population-based phone interview on sleep health among adults (≥18 years) from Canada. Self-reported bedtime screen use (in bed or within 1 hour of bedtime) of the past month was used to classify participants into three groups: occasional (<once/week), moderate (1-4 times/week), and regular (≥5 times/week) bedtime screen users. Sleep health (regularity, satisfaction, alertness, timing, efficiency, duration) was measured using the RU-SATED questionnaire. Post-stratified survey weights were computed from the 2021 Canadian census to ensure representativeness of the adult population in terms of geography, biological sex, age, and ethnicity. RESULTS: The sample included 1342 adults (51.5% females; 41.7% between 40-64 years) and 45.3% reported bedtime screen use every day. After accounting for biological sex, age, and income, both occasional and regular screen users reported the best overall sleep health. Results varied by sleep health dimension and biological sex was a moderator of the bedtime screen use and sleep regularity association. Bedtime screen use frequency was significantly associated (p = .01) with sleep regularity only among males. CONCLUSIONS: The association between bedtime screen use and sleep health appears complex as bedtime screen use frequency, the sleep health dimension measured, and biological sex can all influence this relationship. More research is needed to understand the sleep health and bedtime screen use association and moderators of this relationship in adults.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it