National Evaluation of Canadian Multi-Service FASD Prevention Programs: Interim Findings from the Co-Creating Evidence Study
Bibliographic record
Abstract
Since the 1990s, a number of multi-service prevention programs working with women who have substance use, mental health, or trauma and/or related social determinants of health issues have emerged in Canada. These programs use harm reduction approaches and provide outreach and "one-stop" health and social services on-site or through a network of services. While some of these programs have been evaluated, others have not, or their evaluations have not been published. This article presents interim qualitative findings of the Co-Creating Evidence project, a multi-year (2017-2020) national evaluation of holistic programs serving women at high risk of having an infant with prenatal alcohol exposure. The evaluation utilizes a mixed-methods design involving semi-structured interviews, questionnaires, focus groups, and client intake/outcome "snapshot" data. Findings demonstrated that the programs are reaching vulnerable pregnant/parenting women who face a host of complex circumstances including substance use, violence, child welfare involvement, and inadequate housing; moreover, it is typically the intersection of these issues that prompts women to engage with programs. Aligning with these results, key themes in what clients liked best about their program were: staff and their non-judgmental approach; peer support and sense of community; and having multiple services in one location, including help with mandated child protection.
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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 | 0.075 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".