Challenging sleep-wake behaviors reported in informal, conversational interviews of caregivers of children with fetal alcohol spectrum disorder
Bibliographic record
Abstract
Objective: Sleep complaints are clinically expected in children exposed to alcohol during pregnancy. We aim to reveal patterns of association among sleep–wake behaviors that are challenging in the life of children with fetal alcohol spectrum disorders (FASD).Methods: Through text-mining analyses, we numericized the transcripts of 59 caregiver’s informal, conversational interviews. That is, the relative frequencies-of-occurrences of words as well as their semantic specificities (italic) were clustered, categorized, and visualized for patterns.Results: A total of 4008 words were indexed where sleep took the 91st place of most important words. Sleep and wake were however not associatively conversed throughout the interviews. Sleep-related words conversed were: night, nap, apnea, asleep, awake, bed, bedroom, bedtime, mattress, melatonin, overnight, and wake-up. Among some FASD-characteristic words describing the challenges were: huge, alcohol, manage*, stop, adopt, crazy*. The semantic space reflecting these challenges experienced in caring for children with FASD was divided into two axes: child-oriented vs. other-oriented words, and day-related and night-related words. The position of sleep shows that problematic sleep was expressed as a ‘family’ issue. Clumsy* was interrelated with problematic sleeping and waking. Despite that mostly night was associatively conversed, the association of Routines, Managing, and Planning with sleep underscores the challenges faced.Conclusion: When conversing caregivers of children with FASD seldom interrelate ‘sleep’ but rather ‘night’ with FASD-characteristics. Increased sleep awareness combined with educational initiatives regarding sleep are advocated.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".