Infantile atopic dermatitis – increasing severity predicts negative impacts on maternal and infant sleep: a mixed methods study
Bibliographic record
Abstract
Abstract Background While the impacts of atopic dermatitis (AD) on maternal and child sleep outcomes have been previously explored, less is known about the associations between infantile AD and sleep quality and quantity. Objective To describe the perceived causes of AD-associated maternal sleep disturbances and the association between AD severity and infant sleep outcomes. Methods Mothers with infants aged < 19 months old with a diagnosis of AD were recruited from social media and medical clinics in Winnipeg, Canada between October 2021 and May 2022. Infant AD severity was classified using maternal-reported data on the Patient-Oriented Scoring Atopic Dermatitis tool (PO-SCORAD). Quantitative data were collected via a series of questionnaires with a subset of mothers subsequently completing semi-structured interviews. Quantitative and qualitative data were integrated in the discussion. Results Mothers of infants with moderate/severe AD (6/12) were more likely to report their infant suffering from a higher degree of sleeplessness (i.e., ≥ 5 on a scale of 0–10) over the past 48 h compared to mothers of infants with mild AD (0/18). This was supported by qualitative findings where mothers described how their infant’s sleep quality and quantity worsened with AD severity. Additionally, 7/32 mothers reported that their child’s AD, regardless of severity, disturbed their sleep. Maternal sleep loss was most commonly attributed to infant itching (6/7), followed by worry (4/7). Conclusion Infantile AD severity was associated with worse sleep outcomes for both mothers and infants. We propose that maternal and infantile sleep quality and quantity can be improved by reducing AD severity through adherence to topical treatments.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".