Development and Psychometric Evaluation of the Preconception Health Knowledge Questionnaire
Bibliographic record
Abstract
PURPOSE: To develop and psychometrically test a comprehensive measure of preconception health knowledge. DESIGN: Cross-sectional survey, in May and June, 2019. SETTING: Alberta, Ontario, and Québec, Canada. SAMPLE: One thousand seven hundred seventy-seven women and men with ≥1 children born in the last 5 years or planning a pregnancy in the next 5 years. MEASURES: Using prior literature and input from public health nurses and physicians, the Preconception Health Knowledge Questionnaire (PHKQ) was developed and comprised 25 multiple choice questions on reproductive history, sexual health, infectious diseases, chronic medical conditions, mental health, medications, immunizations, lifestyle behaviors, psychosocial stressors, and environmental exposures. ANALYSIS: Psychometric testing was undertaken to evaluate item difficulty, discrimination, quality of response alternatives, internal consistency, and construct validity. RESULTS: Participants had a mean total score of 15.8/25 (SD = 3.9); women and men had mean total scores of 16.2 (SD = 3.6) and 13.8 (SD = 4.7), respectively. Most items were neither too difficult nor too easy, discriminated well between participants with high and low knowledge, and had appropriate response alternatives. High internal consistency (KR-20 = 0.87) and construct validity, shown via significant correlations with education level and previous preconception care receipt, were demonstrated. CONCLUSION: The PHKQ is a reliable and valid tool for measuring preconception health knowledge and may be useful in identification of high-risk groups in need of preconception health education and evaluation of preconception health interventions.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".