Existing standardised questionnaires do not adequately capture quality‐of‐life outcomes of greatest importance for those living with type 1 diabetes in pregnancy
Bibliographic record
Abstract
BACKGROUND: No standardised questionnaires have been specifically developed to assess the considerable demands of managing type 1 diabetes (T1D) during pregnancy. AIMS: This study aimed to explore what domains of measurement are important to quality of life during pregnancy with TID and to assess if standardised questionnaires, used by previous researchers, adequately capture patients' reported experience of TID in pregnancy. METHODS: A qualitative inquiry was conducted using semi-structured focus groups with Canadian women who have experienced T1D in pregnancy. Participants were asked open-ended questions about experiences managing T1D during pregnancy and whether options on standardised tools captured their pregnancy experiences. Audio from focus groups was transcribed verbatim. Two researchers independently analysed the transcripts using inductive thematic analysis. Salient ideas, experiences and key words were coded iteratively and grouped into broader themes and subsequently reviewed by five participants. RESULTS: The sample included nine participants. Emergent themes included changes in day-to-day routines to manage T1D in pregnancy, fear of hyperglycaemia during pregnancy and of hypoglycaemia postpartum. Participants felt that existing options on standardised questionnaires did not adequately quantify diabetes interference in work, family time, planned activities and sleep, and did not address hyperglycaemia fear. CONCLUSIONS: Existing standardised questionnaires do not adequately capture patient-reported outcomes of greatest importance for those living with T1D in pregnancy. Future research assessing the impact of therapies on quality-of-life measures in TID pregnancies should quantify their influence on day-to-day activities, adjust measures of sleep quality and capture fear of hyperglycaemia in pregnancy and hypoglycaemia postpartum.
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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 | 0.029 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".