Measurement of Preconception Health Knowledge: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Preconception health is an important determinant of maternal, paternal, and infant outcomes. Knowledge is commonly used to evaluate the effectiveness of interventions to promote preconception health. Our objective was to examine how preconception health knowledge has been measured in the existing literature and to identify measurement gaps, biases, and logistical challenges. DATA SOURCE: MEDLINE, EMBASE, PsycINFO, CINAHL, the Cochrane Database of Systematic Reviews, and gray literature were searched from database inception to January 2018. STUDY INCLUSION AND EXCLUSION CRITERIA: Studies were included if they measured preconception or interconception health knowledge and included reproductive-aged women and/or men. DATA EXTRACTION: Two independent reviewers completed data extraction and quality appraisal using standardized instruments. DATA SYNTHESIS: Due to measurement heterogeneity, a narrative synthesis was performed. RESULTS: The review included 34 studies from 14 countries with data collected in 2000 to 2017. Most studies used cross-sectional (n = 24) or prepost designs (n = 7). Studies primarily sampled women (n = 25), and methodological quality was rated largely as weak (n = 18) or moderate (n = 14). Preconception health knowledge tools focused on fertility, folic acid, and alcohol, with few questions pertaining to men's health, mental health, or the interconception period. Only 19 (56%) studies reported psychometric properties of their knowledge tools. CONCLUSIONS: This systematic review revealed the need for a valid and reliable knowledge tool that reflects a holistic conceptualization of preconception 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 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 | 0.032 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".