0255 Smartphones In The Bedroom, Sleep, Communication, And Mental Health In Australian School Students
Bibliographic record
Abstract
Smartphones in the bedroom rob sleep time and facilitate communication during the circadian low. These factors may lead to impaired communication and disturbed mental health. However, nighttime messaging may also foster social connection. This was investigated in a large dataset of Australian students. The Resilient Youth Australia Limited dataset includes more than 180,000 Australian primary and high school students, who completed a 30-60min in-class survey with questions about phone use, sleep, friendships, and the General Health Questionnaire (GHQ-12). Respondents with complete datasets were included in analyses (n=169,352, 7-18y, mean=13 ± 3y, male=49.9%, gender diverse=0.4%). Using phones at night (10pm-6am) at least once in the past week was reported by 55% of respondents. The proportion increased with age, from 25% at 7-8y to 83% by 17-18y. Forty percent reported that they never or only sometimes had 8h or more sleep per night. A third reported that they had responded to a text in anger, 25% that they had received hurtful messages, and 22% that they had been cyberbullied at least once in the past month. A quarter reported that they built friendships not at all or only sometimes. Controlling for age and gender, nighttime phone use was associated with significantly increased odds of: responding in anger (Odds Ratio, OR=4.92, 95%Confidence Intervals, CI=4.80–5.06); receiving hurtful messages (OR=3.96, CI=3.85–4.07); and being cyberbullied (OR=2.78, CI=2.71–2.86). It was also associated with reduced odds of getting 8h or more sleep per night (OR=0.53, CI=0.45–0.56), and higher (less favourable) GHQ-12 scores (13.60 ± 7.14 compared to 10.87 ± 6.52, p<0.01, d=0.4). The odds of building friendships increased (OR=1.18, CI=1.15,1.20). Using cell phones at night is common among children as young as 7y. This may not only impact negatively on sleep, but may also increase angry or hurtful communication, and mental health issues. On the other hand, it may also facilitate friendship building. Interventions to reduce phone use at night must consider the benefits and potential losses associated with change. Stephanie Centofanti is supported in part by Resilient Youth Australia Limited post-doctoral funding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".