"New Choices" for women with addictions: perceptions of program participants
Bibliographic record
Abstract
BACKGROUND: Substance use in pregnancy is a major public health problem. It can have profound effects on pregnancy outcomes, and childhood health and development. Additionally, women who use substances have their own health-related issues. Although intervention is important, these women often have difficulty using traditional systems of care. The New Choices program is a centralized, multi-sector approach to service delivery that has attempted to overcome barriers to care by offering one-stop shopping in a supportive environment. As part of an evaluation of this program designed for women who are pregnant and/or parenting young children, interviews were conducted with participants to gain insight into their experiences in New Choices and perceptions of any changes attributed to program involvement. METHODS: A qualitative, exploratory design was used to guide data collection and analysis. Four women participated in a focus group interview and seven women agreed to individual interviews over the course of the program evaluation (N = 11). A semi-structured interview guide was used to explore women's experiences in New Choices and their perceptions of the program and its impact. The interview data were analyzed using NVivo software and an inductive approach to data analysis. RESULTS: The emergent themes captured women's motivations for attending New Choices, benefits of participation, and overall quality of the program. Children were the primary motivating factor for program enrollment. Perceived benefits included decreased substance use, improved maternal health, enhanced opportunity for employment, increased access to other resources, enhanced parenting skills, and improved child behaviour and development. Women highly valued the comprehensive and centralized approach to service delivery that provided a range of informal and formal supports. CONCLUSIONS: Interview findings endorse the appropriateness and potential efficacy of a collaborative, centralized approach to service provision for women with substance use issues. Although the findings provide insight into an alternative model of service delivery for women with addictions, future research is needed to evaluate the effectiveness of the intervention. Research also is needed to determine which program components or constellation of components contribute to desired outcomes, and to learn more about processes that underlie changes in behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".