Behavioural Interventions for Sleep: Who Prefers what?
Bibliographic record
Abstract
Many university students have trouble sleeping because their minds are too active with worries and other sleep disruptive thoughts they are unable to control. Previous research has compared two self-help intervention: Structured Problem-solving, which involves scheduling time earlier in the day to write out worries and steps toward solutions; and Beaudoin’s Somnotest APP, which uses mental imagery to prevent sleep disruptive thoughts. Both interventions were equally effective alone or in combination. Nevertheless, there were individual differences in how students responded to the interventions. Our study extends previous research by examining these individual differences. We examined students’ preferences for interventions in relation to their circadian preference (morning types and evening types) and their preferred way of coping with stress (i.e, emotion focused vs. problem focused). We predict that students who prefer problem-focused coping will also prefer Structured Problem-solving, whereas those who prefer emotion-focused coping will favour the APP. Since evening types take longer to fall asleep, we predict that they may find the APP less effective because it could be arousing. Participants consisted of 131 MacEwan University students who were poor sleepers. They completed standardized measures of sleep and arousal (Sleep Quality Scale, Glasgow Sleep Effort Scale and Pre-Sleep Arousal Scale), ways of coping with stress (COPE) and circadian preference ( Composite Scale of Morningness). Data analysis will be completed by April. Results and implications will be discussed. Discipline: Psychology Faculty Mentor: Dr. Nancy Digdon
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".