199 COVID-19 Pandemic Sleep and Dreams at the US-Mexico Border
Bibliographic record
Abstract
Abstract Introduction The impact of the COVID-19 pandemic in the border region is not well-described, including the impact of pandemic-related sleep disturbances on dream experiences, despite frequent reports of meaningful changes to dreams in the population. Methods Participants were 155 individuals who completed the Nogales Cardiometabolic Health and Sleep (NOCHES) Study and a COVID sub-study (95% Hispanic/Latino). Participants were asked whether, as a result of the pandemic, they have experienced more schedule regularity, improved/worsened sleep, more initial or middle-of-the-night insomnia, more sleepiness, and more napping. They were also asked whether they experienced more, fewer, or the same amount of dreams in general, positive dreams, and negative dreams. Multinomial logistic regressions were used to examine overall, positive, and negative dream recall (more or less vs same) as outcome and perceived change in sleep as independent variable, adjusted for age, sex, socioeconomics, and mental health symptoms (assessed with PHQ4). Results Those who reported more schedule regularity were less likely to report more negative dreams (Relative Risk Ratio [RRR]=0.40, p=0.010). Those who reported improved sleep were also more likely to report more positive dreams (RRR=3.97, p=0.004). Those with worsened sleep were more likely to report fewer dreams overall (RRR=2.23, p=0.037), fewer positive dreams (RRR=2.24, p=0.003) and more negative dreams (RRR=3.69, p<0.0005). Those with more initial insomnia were more likely to report fewer positive dreams (RRR=2.43, p=0.002) and more negative dreams (RRR=4.12, p<0.0005). Those with more middle-of-the-night insomnia reported fewer dreams overall (RRR=2.35, p=0.018), fewer positive dreams (RRR=2.55, p=0.001), and more negative dreams (RRR=5.01, p<0.0005). Those with more daytime sleepiness were more likely to report fewer dreams overall (RRR=4.75, p<0.0005), fewer positive dreams (RRR=1.92, p=0.019), and more negative dreams (RRR=3.91, p<0.0005), and were less likely to report more positive dreams (RRR=0.26, p=0.018). Those who reported napping more were more likely to report fewer dreams overall (RRR=2.78, p=0.008), fewer positive dreams (RRR=2.10, P=0.008), and more negative dreams (RRR=2.83, p=0.003), and were less likely to report more positive dreams (RRR=0.16, p=0.004). Conclusion Those whose sleep worsened due to the pandemic reported less dream recall, and dream content that was more negative and less positive overall. Support (if any) R01MD011600, R01DA051321
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".