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Record W4220735510 · doi:10.1177/17446295221079584

“We’re in the same book, but we’re in different parts of the book”: Dominant and sub-group narratives of life following a Down syndrome determination

2022· article· en· W4220735510 on OpenAlexafffund
Sarah L. Sangster, Kailey J. DeLucry, Karen Lawson

Bibliographic record

VenueJournal of Intellectual Disabilities · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNarrativeFraming (construction)PsychologyDevelopmental psychologyDiversity (politics)Social psychologySociologyHistoryLiterature

Abstract

fetched live from OpenAlex

Parents of children with Down syndrome (DS) were interviewed about if their experiences raising their children matched their initial assumptions about parenting a child with DS. A dominant narrative was identified, wherein most parents described initially having negative assumptions, which did not come to fruition; parenting their child was not very different from parenting a typical child. There was also a sub-group of participants who disputed the dominant narrative; parenting their child was challenging and the dominant narrative marginalizes that experience. The findings indicate that although for many parents, having a child with DS is like “ taking the scenic route” (i.e., it involves a few more hurdles, but often more rewards), this framing is not always applicable. Therefore, health care providers and support organizations should promote a narrative that encompasses the diversity of parenting a child with DS.

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)0.005
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.014
Scholarly communication0.0050.006
Open science0.0010.006
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.318
Teacher spread0.268 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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