Validating PreCHAT: A Digital Preconception Health Risk Assessment Tool to Improve Reproductive, Maternal and Child Health
Bibliographic record
Abstract
STUDY BACKGROUND: Despite the growing understanding of preconception care, numerous barriers to its delivery still exist, including a lack of evidence-based, accessible screening tools. PURPOSE: To validate a new digital Preconception Health Assessment Tool (PreCHAT) against the current best practice, physician-delivered tool in Ontario, Canada, and explore how PreCHAT's design impacts its risk identification abilities relative to the comparison tool. METHODS: A criterion validation study was conducted with 53 female participants aged 18-44 years. Participants completed both tools in a controlled setting. PreCHAT was completed on a tablet individually by participants, while the comparison tool was administered by a physician. Three physicians administered the comparison tool. Measures of strength of agreement between PreCHAT and the comparison tool were calculated using percent agreement, Cohen's Kappa, and prevalence-adjusted and biased-adjusted kappa (PABAK). RESULTS: PreCHAT identified 135 individual risk factors, while the comparison tool identified 102. Both tools shared the same 14 domains of preconception care and 88 risk factors; of the 88 risk factors, PreCHAT identified an average of 3.42 (p < 0.0001) more risks per participant than the comparison tool. PABAK scores indicated almost perfect agreement between PreCHAT and the comparison tool. CONCLUSIONS: This study suggests that PreCHAT is valid against the current best practice tool and is broader in its risk identification among individuals of reproductive age. PreCHAT's patient-facing, digital, EMR-integrated design may offer unique benefits to providers and patients. PreCHAT offers providers an innovative approach to deliver preconception care and may positively impact reproductive, maternal, and child health.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".