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Record W2145045657 · doi:10.3402/ijch.v70i4.17844

Developing effective, culturally appropriate avenues to FASD diagnosis and prevention in northern Canada

2011· article· en· W2145045657 on OpenAlexaffabout
Amy Salmon, Sterling K. Clarren

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2011
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStakeholderContext (archaeology)Service (business)Public relationsService providerService delivery frameworkMedicineMedical educationPolitical scienceBusinessNursingGeographyMarketing

Abstract

fetched live from OpenAlex

This article describes 2 research initiatives that are being undertaken by members of the Canada Northwest FASD Research Network, involving collaborations between researchers, clinicians, service providers and community members in the Canadian North. Improving both the diagnosis and prevention of FASD requires evidence-based approaches to clinical and social service delivery that are capable of accounting for the unique contours of the geographic, regional and cultural diversities in which women become pregnant and in which families live. Although FASD has been a priority for communities and governments in northern Canada, research capacity has not been available to support the development of the context-specific knowledge needed to inform policy and practice in this region. Moreover, there have not been adequate mechanisms for transferring practice-based knowledge from the Canadian North to researchers and service providers in the South, who might make use of this knowledge to inform their own practice. Herein, we highlight the ways in which reciprocal knowledge exchange involving CanFASD Northwest researchers at academic health science centres and diverse stakeholder groups is supporting multi-directional capacity building in FASD diagnosis and prevention.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.301
Teacher spread0.278 · 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

Citations32
Published2011
Admission routes2
Has abstractyes

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