The relationship of trait mindfulness to physical and psychological health during pregnancy
Bibliographic record
Abstract
INTRODUCTION: Research on mindfulness has extended to the prevention of psychopathology and physical conditions during pregnancy. The purpose of this study was to investigate the relationship between trait mindfulness assessed in the first or early second trimester to health outcomes throughout pregnancy. METHODS: A total of 510 women were recruited at McGill University-affiliated obstetrics clinics (average gestational age: 13.43 weeks, sd = 1.2). The Mindful Awareness and Attention Scale (MAAS) was administered at baseline. At three timepoints during pregnancy, participants completed the Perceived Stress Scale (PSS-10), the Edinburgh Postnatal Depression Scale (EPDS), the Prenatal Distress Questionnaire-revised (PDQR) and a measure of pregnancy symptom intensity and indicated whether they had been diagnosed with gestational diabetes or high blood pressure. RESULTS: Higher MAAS scores predicted lower PSS, EPDS and PDQR scores and less severe physical discomforts throughout pregnancy. MAAS scores were a stronger predictor of PSS scores earlier in pregnancy. Logistic regressions found that trait mindfulness did not predict the presence of physical discomforts, diabetes or high blood pressure. CONCLUSIONS: These results indicate that trait mindfulness is an important predictor of subjective stress, depression, anxiety and the severity of physical discomforts during pregnancy. These findings suggest that interventions earlier in pregnancy may increase the impact of mindfulness on maternal health.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".