Respiratory Syncytial Virus Bronchiolitis Hospitalizations in Young Infants After the Introduction of Paid Family Leave in New York State, 2015‒2019
Bibliographic record
Abstract
Objectives. To determine if the introduction of New York State’s 8-week paid family leave policy on January 1, 2018, reduced rates of hospitalizations with respiratory syncytial virus (RSV) bronchiolitis or any acute lower respiratory tract infection among young infants. Methods. We conducted an interrupted time series analysis using New York State population-based, all-payer hospital discharge records, October 2015 to December 2019. We estimated the change in monthly hospitalization rates for RSV bronchiolitis and for any acute lower respiratory tract infection among infants aged 8 weeks or younger after the introduction of paid family leave while controlling for temporal trends and RSV seasonality. We modeled RSV hospitalization rates in infants aged 1 year as a control. Results. Hospitalization rates for RSV bronchiolitis and any acute lower respiratory tract infection decreased by 30% after the introduction of paid family leave (rate ratio [RR] = 0.71; 95% confidence interval [CI] = 0.54, 0.94; and RR = 0.72; 95% CI = 0.59, 0.88, respectively). There were no such reductions in infants aged 1 year (RR = 0.98; 95% CI = 0.72, 1.33; and RR = 1.17; 95% CI = 1.03, 1.32, respectively). Conclusions. State paid family leave was associated with fewer RSV-associated hospitalizations in young infants. (Am J Public Health. 2022;112(2):316–324. https://doi.org/10.2105/AJPH.2021.306559 )
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".