Trajectories of sleep disturbance among school teachers in Shenzhen, China: patterns, predictors, and mental health correlates
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the trajectories of sleep disturbance among school teachers in China, the factors that influencing these trajectories, and their association with mental health outcomes. METHODS: The participants in this study were 3,634 teachers from Shenzhen city, China, with 21.2% of them being males and a mean age of 34.69 years. The survey was conducted in three waves between April 2021 and June 2022, and assessed sleep disturbance, neuroticism, resilience, negative life events, depression, anxiety, and demographic variables. Latent Growth Mixture Modeling (LGMM) was used to identify different latent trajectory classes of sleep disturbance. Multivariable logistic regression was then employed within the best-fitting LGMM to examine the associations between trajectory membership with both predictors and mental health outcomes. RESULTS: The prevalence of sleep disturbance among school teachers in the three surveys was 19.0%, 16.6%, and 18.8%, respectively. Four trajectories of sleep disturbance were identified within one year: no/low (73.3%), persistent (16.2%), new-onset (5.3%), and remission (5.2%). Higher neuroticism and lower resilience were associated with increased risks of persistent or new-onset sleep disturbance. A greater number of negative life events was a potential risk factor for new-onset sleep disturbance, whereas younger age protected against persistent sleep disturbance. Additionally, teachers in the persistent and new-onset trajectories were more likely to report depression and anxiety. CONCLUSIONS: Although most teachers' sleep disturbance remains mild or resolved over time, a subset of teachers, particularly those with the aforementioned risk factors, experience persistent or new-onset sleep disturbance. Therefore, targeted interventions for these teachers are warranted.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".