Pregnancy-related diastasis rectus abdominis: Impact of a multi-component group-based intervention
Bibliographic record
Abstract
Purpose: To explore the feasibility and effect on outcomes of a one-time multi-component group-based intervention among women with pregnancy-related diastasis rectus abdominis (DRA).Methods: Women with clinically diagnosed DRA and minimum 8 weeks postpartum participated in a pre-post cohort pilot study.Subjects participated in a group workshop consisting of education and exercise prescription.They were assessed before the workshop and 8 weeks later with a booster session at 4 weeks.The following assessments were used: inter-recti distance (finder width), linea alba (LA) integrity, LA tension generating capacity, active straight leg raise (ASLR), Pelvic Floor Disability Index (PFDI-20), and global rating of change scale(GRC).Results: Thirty participants were enrolled in this study and 16 completed both pre and post measurements (53.3%).Following intervention, all outcomes measures improved with statistically significant changes in IRD (finger width), LA integrity, and LA tension generation.The average GRC score was 1.7.Issues with loss to follow up point to lack of feasibility of this intervention in its current format.Conclusion: We found one-time multi-component group-based intervention improved pregnancy-related DRA outcomes.Future studies need to further explore the effect of the different components within this intervention, particularly behavioural strategies.Further, the benefit of applying self-management principles in DRA interventions as well as further investigating assessment techniques is also warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".