Fear of High Blood Sugar in Pregnancy Questionnaire: development of a person-reported outcome measure to assess fear of hyperglycaemia in pregnant women with type 1 diabetes
Bibliographic record
Abstract
Abstract Background Although newer diabetes technologies improve glycaemic outcomes in type 1 diabetes pregnancies, most studies have not demonstrated improvements in scores on person-reported outcome measures (PROMs). This is likely because commonly used PROMs do not effectively capture quality-of-life concerns of greatest importance to pregnant women with type 1 diabetes. Aims We aimed to develop a research questionnaire to evaluate fear of hyperglycaemia in type 1 diabetes pregnancies, which is an important person-reported outcome in this population. Methods The format of the questionnaire was modelled after that of the Hypoglycaemia Fear Survey-II. Items pertaining to fear of hyperglycaemia, specifically in pregnancy, were developed. The questionnaire was refined using cognitive interviewing among Canadian women with experience of type 1 diabetes in pregnancy. We recruited participants sequentially and modified our questionnaire iteratively based on participants’ feedback. Saturation was reached when no significant changes were made by three consecutive participants. Results The questionnaire was reviewed by nine participants with median type 1 diabetes duration of 22 years. The questionnaire asks about 16 behaviours to avoid hyperglycaemia in pregnancy and 12 worries related to hyperglycaemia in pregnancy. 5-point Likert scales are used to quantify frequency of behaviours and worries. Conclusions Type 1 diabetes management is challenging in pregnancy. One source of stress for women is the fear of hyperglycaemia and its effects on their unborn children. The Fear of High Blood Sugar in Pregnancy Questionnaire is a pregnancy-specific person-reported outcome measure which can begin to quantify the impact of diabetes management strategies on hyperglycaemia fear.
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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 | 0.005 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".