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Record W2015171705 · doi:10.5663/aps.v3i1-2.18813

The Health, School, and Social Outcomes of Off-Reserve First Nations Children of Teenage Mothers

2014· article· en· W2015171705 on OpenAlexaffvenueabout
Anne Guèvremont, Dafna Kohen

Bibliographic record

Venueaboriginal policy studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of OttawaStatistics Canada
Fundersnot available
KeywordsTeenage pregnancyDemographyPsychologyMedicineDevelopmental psychologyPopulationSociology

Abstract

fetched live from OpenAlex

Children of teenage mothers differ in their health, social, and educational outcomes compared to children of older mothers. Even though the teen birth rate for First Nations women in Canada is higher than the national teen birth rate, there has been little research examining the outcomes of off-reserve First Nations children born to mothers who began childbearing in their teen years. Using data from the 2006 Aboriginal Peoples Survey, this study examined the health, social, and educational outcomes of off-reserve First Nations children, aged six to fourteen, who were born to teenage mothers, as compared to those born to older mothers. Off-reserve First Nations children of teenage mothers were more likely to be rated by their mothers as having dental problems, more likely to have failed a grade, less likely to be rated as doing very well in school, and less likely to have maternal reports of school satisfaction. They were also more likely to be rated as not getting along well in the last six months with their teachers, parents, and siblings. Although some of these differences were explained by socio-economic characteristics (getting along with teachers and parents, doing well in school), differences in all three domains (dental problems, getting along with parents, grade failure and parental school satisfaction) remained. Recommendations for future research are discussed.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.991

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 studies0.0100.001
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 head0.018
GPT teacher head0.388
Teacher spread0.370 · 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 designNot applicable
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

Citations0
Published2014
Admission routes3
Has abstractyes

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