Complementary therapies in substance use recovery with pregnant women and girls
Bibliographic record
Abstract
Objectives:Supporting women and girls who are pregnant and experiencing substance use challenges is a high priority for researchers, clinicians, and policymakers. Complementary therapies (CTs) can be effective forms of treatment in some contexts and populations; however, research on the use of CTs in substance use recovery with pregnant women and girls is scarce. To fill this gap, we conducted a mixed methods study using survey data collected at a women’s recovery center in Canada. Our objectives were to describe CTs provided at the program; identify what CTs are perceived by participants as most/least positive; and explore factors that may impact participant experiences with CTs.Methods:We analyzed feedback responses from 255 women and girls (Mage = 27.5 years, range 15–64) using Pearson chi-square tests, logistic regression, and inductive content analysis.Results:The most frequently provided CTs were yoga, energy-related activities (e.g. reiki, reflexology), and meditation. Among the most common CTs, participants provided the highest endorsements for massage and physical activity, and the lowest endorsements for yoga and drumming. Across CTs, whether participants looked forward to an activity contributed significantly to whether they found it helpful, would like to do it again, and planned to continue engaging in the activity after leaving the program. Four broad contextual factors were identified that may impact experiences and perspectives about CTs: (1) goodness of fit, (2) self-awareness, (3) growth, and (4) healing and holistic wellbeing.Conclusions:This study provides novel evidence on the potential impacts of CTs in substance use treatment for pregnant women and girls, and important contextual factors to consider when implementing these approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".