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Record W4407805712 · doi:10.1093/bjsw/bcaf014

A scoping review of birth alerts: A Canadian context

2025· review· en· W4407805712 on OpenAlexaffabout
Danielle Elke, Peter Choate, Christina Tortorelli

Bibliographic record

VenueThe British Journal of Social Work · 2025
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMount Royal UniversityUniversity of Calgary
Fundersnot available
KeywordsGrey literatureContext (archaeology)WelfareIndigenousSystematic reviewSocial workCriminologyChild protectionCriminal justiceSocial WelfarePolitical sciencePublic relationsPsychologyGeographyLawMEDLINE

Abstract

fetched live from OpenAlex

Abstract This scoping review examines the history and application of birth alerts in Canada, from social work, legal, policy, and social justice lenses. In Canada, child welfare authorities developed a practice commonly known as birth alerts. This evolved across the country in an uneven fashion but has certainly been active since the early 2000s. There are many criticisms including causing undue trauma with children and families and disrupting early attachment as well as focus on Indigenous women in Canada as an example of racial bias (Doenmez et al. 2022). Since the release of the National Inquiry into Missing and Murdered Women and Girls (2019) report, provinces and territories have ended the practice. Literature reviewed examine birth alerts, pre-birth child welfare involvement and child welfare involvement at the birth of a child. Primarily, Canadian sources are used; however, countries with similar child welfare structures such as Australia, the UK, and the USA are considered. Studies using various methods have been included: qualitative, quantitative, mixed-method, systematic reviews, grey literature, and case law. This review uses the JBI Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methods for scoping reviews (Peters et al. 2015).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.874
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.357
Teacher spread0.317 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations3
Published2025
Admission routes2
Has abstractyes

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