Research and policy priorities for addressing prenatal exposure to opioids in Alaska
Bibliographic record
Abstract
The current opioid crisis in Alaska and the USA will negatively affect the health and wellbeing of future generations. The increasing number of infants born with neonatal opioid withdrawal syndrome (NOWS) has had a profound impact on families, health care providers and the child welfare system. This manuscript summarises the main themes of a Symposium held in Anchorage, Alaska with health care providers, researchers, elders and public health officials that focused on identifying emerging challenges, trends and potential solutions to address the increasing number of infants and children affected by maternal opioid use. Five areas of importance for research and policy development that would direct improvement in the care of infants with NOWS in Alaska are outlined with the goal of supporting a research agenda on opioid misuse and child health across the circumpolar north. Abbreviations: NOWS - neonatal opioid withdrawal syndrome; NAS - neonatal abstinence syndrome; MAT - medication-assisted treatment; NICU - neonatal intensive care unit; OATs - opioid agonist treatments; OCS - office of children's services; ANTHC - Alaska Native Tribal Health Consortium; OUD - opioid use disorder; SBIRT - screening, brief intervention and referral to treatment; ISPCTN - IDeA States Pediatric Clinical Trials Network; NIH - National Institutes of Health; ANMC - Alaska Native Medical Center; DHSS - Department of Health and Social Services; AAPP - All Alaska Pediatric Partnership.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".