Authors’ reply: Comment on: The joy of parenting: infant sleep intervention to improve maternal emotional well-being and infant sleep
Bibliographic record
Abstract
We thank the author of this letter We would like to make a number of points in response. The first is regarding the type of intervention utilised. We would like to refer interested readers to our published review, in which we defined a behavioural intervention as "a practice implemented by a parent or primary care-giver with the primary aim of improving infant sleep. These behaviours typically include ways of settling the baby at sleep time, how and when to respond to infant crying or signalling during a period of sleep, and other strategies to promote undisturbed sleep". This is a narrow and clear definition. As most clinicians would be aware, the presentation of each infant and their parents is unique. The precise plan that is given to parents varies according to the infant's age, weight, current feeding habits (e.g. overnight feeds, quantity of solids) and the parents' attitudes toward settling behaviours and degree of comfort with extinction-based strategies. As the specific advice was individualised for each family, we cannot easily define the specific intervention in any more detail than has been given. For example, the precise intervention for a four-month-old still requiring overnight feeds would be different from that for an 11-month-old infant who is well established on solids and of healthy weight.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.045 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".