1106 SUBJECTIVE AND OBJECTIVE SLEEP QUALITY INDICATORS AND THE RELATIONSHIP TO POSTPARTUM DEPRESSION
Bibliographic record
Abstract
Relationships between subjective, but not objective sleep outcomes, and postpartum depression have been observed in small, cross-sectional samples. The purpose of this study was to examine these relationships using a large sample followed prospectively from birth through 6 and 12 weeks postpartum. Data from a RCT of a behavioural sleep intervention with 217 first-time mothers were subjected to secondary analysis. Objective sleep variables (nocturnal sleep, night awakenings, daytime sleep) were measured by actigraphy at 6 and 12 weeks. Subjective sleep quality was measured by the General Sleep Disturbance Scale (GSDS) and mothers’ reports of their sleep as a “small”, “big” or “no” problem. Depressive symptoms were measured with the Edinburgh Postnatal Depression Scale (EPDS). Control variables included group allocation, baseline EPDS and social support. Logistic regression was used to estimate the association between subjective and objective sleep variables and the presence of postpartum depression. Separate models estimated the odds of postpartum depression according to each sleep variable. GSDS scores at baseline were not related to depression; however, GSDS scores at 6 weeks were associated with >3 times the odds of depression (OR=3.56; 95% CI=1.73–7.33). The perception that sleep was a “small” or “big” problem at 6 weeks was associated with >3 (OR=3.40; 95% CI=1.54–7.46) and >8 (OR=8.29; 95% CI=2.41–28.59) times the odds of depression, respectively. There was no association between any objective sleep measures and postpartum depression. Subjective sleep quality indicators are strongly associated with postpartum depression while objective sleep outcomes are not. Sleep complaints may be an important clinical indicator of low mood. Future intervention studies to improve mood in the postpartum could target women’s expectations and appraisal of their sleep via cognitive-behavioural strategies. Funded by the Canadian Institutes of Health Research (Grant No MCT 84658)
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".