Patterns of concomitant prescription, over-the-counter and natural sleep aid use over a 12-month period: a population based study
Bibliographic record
Abstract
STUDY OBJECTIVES: Concomitant patterns of sleep aid use may provide insight for understanding the transition to chronic sleep medication use. Therefore, we sought to characterize the trajectories of concomitant natural product (NP), over-the-counter (OTC), and prescribed (Rx) sleep aid use in a population-based sample over 12-months. METHODS: Self-reported data on the use of NP, OTC, and Rx sleep aids were extracted from a Canadian longitudinal study on the natural history of insomnia (N = 3416, M age = 49.7 ± 14.7 years old; 62% women) at baseline, 6-month, and 12-month. Latent class growth modeling was used to identify latent class trajectories using MPlus Version 7. Participants completed a battery of clinical measures: Ford Insomnia Response to Stress Test, abbreviated Dysfunctional Beliefs and Attitudes about Sleep Scale, Beck Depression Inventory, Insomnia Severity Index and, the Pittsburgh Sleep Quality Index. Associations between class membership and baseline covariates were evaluated. RESULTS: Concurrent sleep aid use fell into six distinct latent class trajectories over a 12-month period: Minimal Use (74.5%), Rx-Dominant (11.3%), NP-Dominant (6.3%), OTC-Dominant (4.3%), Rx-NP-Dominant (2.4%), and Rx-OTC-Dominant (1.1%). The three latent classes with prominent prescribed agent use predicted greater incidence of healthcare professional consultations for their sleep (p < 0.05), poorer sleep quality (p < 0.001), elevated dysfunctional sleep beliefs (p < 0.001), and sleep reactivity (p < 0.001). Compared to the other four latent classes, clinical profiles of Rx-NP-dominant and Rx-OTC-dominant groups endorsed greater severity across measures. CONCLUSIONS: Patterns of sleep aid use may provide insight for identifying individuals who may be vulnerable to inappropriate self-medicating practices.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".