Interest in prenatal stress management training: association with medical risk and mental health
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to document levels of interest in stress management training (SMT) during pregnancy, including differences in interest in SMT across levels of medical risk in pregnancy. We also sought to assess differences in pregnancy-specific stress, prenatal worry and depressed mood across levels of medical risk in pregnancy and investigate predictors of interest in SMT. METHODS: We surveyed 379 English-speaking, pregnant people living in Vancouver, Canada, between November 2007 and November 2010. Questionnaires were administered during the third trimester and assessed interest and preferred format of SMT, pregnancy-specific stress, prenatal worry, depressed mood and medical risk in pregnancy. RESULTS: Interest in stress management training programmes during pregnancy was common, with 32% of participants being quite-to-very interested. Preference was split between self-guided study (41%), group counselling (38%) and one-on-one counselling (34%). Higher pregnancy-specific stress and depressed mood, but not medical risk in pregnancy, were associated with higher interest in SMT. Participants experiencing higher stress levels or lower medical risk were more interested in one-on-one counselling. CONCLUSION: Findings indicate that subjective distress rather than objective circumstances is a better predictor of interest in SMT. Care providers should inquire early-on about interest in SMT during pregnancy and ensure awareness of SMT options.
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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 | 0.004 |
| 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) | 0.002 | 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".