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Record W2945575128 · doi:10.3390/ijerph16101767

National Evaluation of Canadian Multi-Service FASD Prevention Programs: Interim Findings from the Co-Creating Evidence Study

2019· article· en· W2945575128 on OpenAlexaffabout
Deborah Rutman, Carol Hubberstey

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInterimEnvironmental healthService (business)BusinessMedicinePolitical scienceMarketing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.266
GPT teacher head0.479
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2019
Admission routes2
Has abstractyes

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