Behavioural therapy for shift work disorder improves shift workers' sleep, sleepiness and mental health: A pilot randomised control trial
Bibliographic record
Abstract
The present study evaluates the efficacy of behavioural therapy adapted for shift work disorder with a randomised control design in a healthcare population. Forty-three night shift workers (m. age: 34 years; 77% women) experiencing shift work disorder were randomised to either the behavioural therapy for shift work disorder (BT-SWD) or a waiting-list control group offered after the waiting period. Participants completed questionnaires on insomnia, sleepiness and mental health pre- and post-treatment, pre- and post-waiting, and at follow-up, and a sleep diary. As night shift workers alternate between sleeping during the day after their night shifts and transitioning to nighttime sleep on days off, insomnia severity and sleep variables were analysed for daytime and nighttime sleep. The BT-SWD involved sleep restriction therapy, stimulus control and fixed sleep periods in the dark. Statistical analyses were performed under intent-to-treat and per-protocol approaches. Repeated-measures two-way ANCOVA analysis, controlling for age, sex and pre-treatment daytime total sleep time, was performed with Bonferroni corrections, and between-group effect sizes computed. Fourteen participants dropped out after randomisation. Under the intent-to-treat analysis, BT-SWD participants had a significant greater decrease in daytime insomnia severity and an increase in daytime total sleep time at post-treatment than the control group, with large between-group effect sizes (-1.25 and 0.89). These corresponding results were also significant with large effect sizes under the per-protocol analysis. Sleepiness, anxiety and depression levels improved at post-treatment and maintained at follow-up when the BT-SWD treated controls were added to the BT-SWD group. The behavioural therapy for shift work disorder can be used to improve the sleep and mental health of healthcare night workers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".