Supplementary Tables for the article: "Structural Vulnerability Factors and Gestational Weight Gain: A Scoping Review on the Extent, Range, and Nature of the Literature"
Bibliographic record
Abstract
Structural Vulnerability Factors and Gestational Weight Gain: A Scoping Review on the Extent, Range, and Nature of the Literature https://doi.org/10.24072/pcjournal.502 All supplementary tables are indicated in the text with the prefix “S” (e.g., Table S1 for Supplemental Table 1) Table S1: Structural vulnerability factors used for database searches and study screening for a scoping review on the association between structural vulnerability factors and gestational weight gain. Table S2: Search strategy used for each database to identify articles that assessed the relationship between structural vulnerability factors and gestational weight gain. Table S3: Data charting for all studies included in the scoping review. Table S4: Data charting for all studies assessing the association between race/ethnicity and GWG. Table S5: Data charting for all studies assessing the association between age and GWG. Table S6: Data charting for all studies assessing the association between parity and GWG. Table S7: Data charting for all studies assessing the association between marital status and GWG. Table S8: Data charting for all studies assessing the association between income and GWG. Table S9: Data charting for all studies assessing the association between education and GWG. Table S10: Data charting for all studies assessing the association between immigration and GWG. Table S11: Data charting for all studies assessing the association between abuse and GWG.
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 | 0.005 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.765 | 0.119 |
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".