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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".