The joy of parenting: infant sleep intervention to improve maternal emotional well-being and infant sleep
Bibliographic record
Abstract
INTRODUCTION: This study aimed to examine how improving infant sleep impacted the emotional well-being of mothers. METHODS: The participants were 80 mothers of infants aged 6-12 months; they attended a primary care medical clinic in Adelaide, Australia, for assistance with infant sleep problems. Behavioural intervention consisted of a 45-minute consultation, where verbal and written information describing sleep physiology and strategies to improve infant sleep was provided. Mothers were followed up 2-6 weeks later. Mothers rated their confidence (C), pleasure (P) and frustration (F) on a scale from 0 to 10, and completed the Depression Anxiety Stress Scale 21 at each consultation. The number of night-time awakenings and time taken to see an improvement in infant sleep were also reported. RESULTS: There was a significant increase in the C and P scores, and a significant decrease in the F scores (all p < 0.001). The mean total CPF score increased significantly from 14 to 25 (maximum score = 30). There was also a significant decrease in depression, anxiety and stress in the mothers (all p < 0.001). The mean number of maximum night awakenings also decreased significantly, from 4.9 to 0.5 (p < 0.001). The mean time taken to see improved infant sleep, as reported by the mothers, was 2.8 nights. CONCLUSION: A single consultation using a behavioural strategy to improve infant sleep was effective in improving infant sleep and in increasing maternal emotional well-being. In particular, the scores for 'pleasure in being a mother' increased dramatically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".