Development and Preliminary Validation of a Comprehensive Questionnaire to Assess Women’s Knowledge and Perception of the Current Weight Gain Guidelines during Pregnancy
Bibliographic record
Abstract
The aim of this study was to develop and validate an electronic questionnaire, the Electronic Maternal Health Survey (EMat Health Survey), related to women’s knowledge and perceptions of the current gestational weight gain guidelines (GWG), as well as pregnancy-related health behaviours. Constructs addressed within the questionnaire include self-efficacy, locus of control, perceived barriers, and facilitators of physical activity and diet, outcome expectations, social environment and health practices. Content validity was examined using an expert panel (n = 7) and pilot testing items in a small sample (n = 5) of pregnant women and recent mothers (target population). Test re-test reliability was assessed among a sample (n = 71) of the target population. Reliability scores were calculated for all constructs (r and intra-class correlation coefficients (ICC)), those with a score of >0.5 were considered acceptable. The content validity of the questionnaire reflects the degree to which all relevant components of excessive GWG risk in women are included. Strong test-retest reliability was found in the current study, indicating that responses to the questionnaire were reliable in this population. The EMat Health Survey adds to the growing body of literature on maternal health and gestational weight gain by providing the first comprehensive questionnaire that can be self-administered and remotely accessed. The questionnaire can be completed in 15–25 min and collects useful data on various social determinants of health and GWG as well as associated health behaviours. This online tool may assist researchers by providing them with a platform to collect useful information in developing and tailoring interventions to better support women in achieving recommended weight gain targets in pregnancy.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".