Additional file 1 of Sociodemographic and occupational factors influencing pregnant workers’ awareness and utilization of the New York City Pregnant Workers Fairness Act
Bibliographic record
Abstract
Supplementary Material 1: Figure S1. Flowchart illustrating the structure of the questionnaire instrument used to collect data on participants’ awareness and understanding of the PWFA law, as well as accommodations received regardless of PWFA awareness. The questionnaire was set up on REDCap/iPad with branching logics and cannot backtrack after answering a question. Table S1. Results from the count and logit components of the multivariable-adjusted zero-inflated Poisson regression model. The model examines the sociodemographic and occupational factors associated with lacking knowledge of PWFA-eligible accommodations. The dependent variable (outcome) is the number of PWFA-eligible accommodations recognized by a participant (discrete count data, from 0 to 6). The adjusted odds ratio (aOR) from the logit component of the zero-inflated Poisson regression model represents the odds of recognizing zero (none) of the eligible accommodations (i.e., did not recognize any of the six examples of accommodations as PWFA-eligible), compared to the reference group (for categorical predictors) or corresponding to per 1-unit increase in the predictor (for continuous variables such as age). The count component of the model (i.e., Poisson process) derives the effect estimates (β) of the Poisson regression given the outcome is not an excess zero (i.e., recognizing ≥1 PWFA-eligible accommodations); the β estimate represents the change in the expected count of the outcome, compared to the reference group (for categorical predictors) or corresponding to per 1-unit increase in the predictor (for continuous variables). Appendix A - ESPWFA Study Survey (English ver.). English language version of the ESPWFA pilot project pregnancy & work survey.
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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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.829 | 0.101 |
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".