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Record W2018358679 · doi:10.1080/09687599.2014.966804

Public health policy and social support for immigrant mothers raising disabled children in Canada

2014· article· en· W2018358679 on OpenAlexaffabout
Sheila Jennings, Nazilla Khanlou, Chang Su

Bibliographic record

VenueDisability & Society · 2014
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsYork University
Fundersnot available
KeywordsImmigrationPublic policyFocus groupPublic healthSocial supportPsychologyDevelopmental psychologyPolitical scienceEconomic growthNursingSociologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

A literature review regarding the social support of immigrant mothers of disabled children in Canada was undertaken with a focus on settings where supports need to be shored up. An integrative review of published papers and policy reports concerning key aspects of social supports for immigrant mothers and disabled children was undertaken. Immigrant mothers experience numerous barriers to social supports for themselves and their disabled children. Maternal immigrant populations experience unique challenges in the setting of childhood disability as well as the challenges of mothers in the dominant culture. There are negative impacts on maternal health as a result of inadequate policy offerings. Public health policy needs to be refashioned in light of weak systems and supports for both immigrant mothers and disabled children and to acknowledge that the current system poses concerning implications for the long-term health of both groups.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.640

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0090.003
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.061
GPT teacher head0.359
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations26
Published2014
Admission routes2
Has abstractyes

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