Bibliographic record
Abstract
According to the Migration Policy Institute, 2017 data revealed that a historic high 44.5 million people living in the United States (US) were foreign-born [1], more than double the number from 1990 [2]. Since the creation of the Refugee Resettlement Program in 1980, refugee families have settled in the US more than in any other country in the world [3]. In 2018, for the first time, Canada overtook the US in numbers of refugees accepted [1]. Foreign-born people now account for 13.7% of the total US population [1]. Further, a quarter of children in the United States currently live in households with at least one foreign-born parent [4]. These population shifts are important to note because immigrant and refugee families bring cultural influences and health experiences from their home countries which can greatly affect the overall health and well-being of children. For these new arrivals, oral health is often a significant health issue. The severity of dental disease varies with country of origin as well as cultural beliefs that can hinder access to care even once it is available to them [5,6]. As pediatricians and primary care providers, we should acknowledge that oral health is important and impacts overall health. Healthcare providers should be able to recognize oral health problems, make appropriate referrals, and effectively communicate with families to address knowledge gaps in high-risk communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".