An investigation of dispositional mindfulness and mood during pregnancy
Bibliographic record
Abstract
BACKGROUND: Mindfulness courses are being offered to numerous groups and while a large body of research has investigated links between dispositional mindfulness and mood, few studies have reported this relationship during pregnancy. The aim of this study was to investigate this relationship in pregnant women to offer insight into whether an intervention which may plausibly increase dispositional mindfulness would be beneficial for this population. METHODS: A cross-sectional analysis was conducted to explore potential relationships between measures of mindfulness and general and pregnancy-specific mood. A sample of pregnant women (n = 363) was recruited using online advertising and community-based recruitment and asked to complete a number of questionnaires online. RESULTS: Overall, higher levels of mindfulness were associated with improved levels of general and pregnancy-related mood in pregnant women. Controlling for general stress and anxiety, higher scores for mindfulness in (psychologically) healthy women were associated with lower levels of pregnancy-related depression, distress and labour worry but this relationship was not apparent in those with current mental health problems. In participants without children, higher mindfulness levels were related to lower levels of pregnancy-related distress. CONCLUSIONS: These results suggest a promising relationship between dispositional mindfulness and mood though it varies depending on background and current problems. More research is needed, but this paper represents a first step in examining the potential of mindfulness courses for pregnant women. Increasing mindfulness, and therefore completing mindfulness-based courses, is potentially beneficial for improvements in mood during 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".