Clinical Opinion Letters Regarding Breastfeeding and Neonatal Abstinence Syndrome for Child Apprehension Family Court Proceedings
Bibliographic record
Abstract
The accelerating reach of opioid use disorder in North America includes increasing prevalence among pregnant people. In Canada, the rate of Neonatal Abstinence Syndrome (NAS) rose 27% between 2012-2013 and 2016-2017, and it is estimated that 0.51% of all infants now experience NAS after delivery. Pregnant people are a priority population for access to opioid replacement therapy programs. Participation in such programs demonstrates significant commitment to self-care among pregnant people and concern for fetal and infant wellbeing. Participation in opioid replacement therapy often results in family surveillance by Child Protection Services and infant apprehension. Children of Indigenous descent are held in foster care at high and disproportionate rates.The Convention on the Rights of the Child principle of Best Interests of the Child governs family law and child access decisions. The value of breastfeeding for all children and in particular for children recovering from NAS can be a consideration in the Best Interest of the Child. Clinicians with expertise in lactation may support the breastfeeding dyad to remain together by preparing Clinical Opinion Letters for the court. This Insights into Policy presents a how-to description of the content of clinical opinion letters in such cases, including context and process considerations, client background, breastfeeding science, and factors specific to neonatal abstinence syndrome.
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 | 0.008 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 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".