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Record W7071046754

Raising Children with a Developmental Disability: Ghanaian-Canadian Parents Shed Insight

2019· other· en· W7071046754 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2019
Typeother
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRaising (metalworking)InterviewFaithInclusion (mineral)Child development
DOInot available

Abstract

fetched live from OpenAlex

Using hermeneutical phenomenology, this study attempts to answer the question: How do Ghanaian-Canadian parents of children with developmental disabilities understand their child’s disabilities, and what experiences inform this? By interviewing six Ghanaian-Canadian parents of children with developmental disabilities through semi-structured interviews, it was determined that these parents understood disability broadly. Their understandings were influenced by childhood and post-migration experiences. These experiences were marked by stigma, stress, frustration and joy. Subsequently, the experience of having a child with a developmental disability inspired faith and allowed parents to see their own strengths and abilities in reconceptualizing disability. This research has pointed to broader systemic issues when children are transitioning out of the educational system and the lack of resources to support parents. In spite of strides being made to create awareness, attitudes towards persons with disabilities and their caregivers continues to be a concern for parents. Social workers and the research communities are encouraged to form alliances with minority groups to promote awareness and address barriers that continue to limit these parents and their children’s participation in Canadian society.

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)0.005
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 score0.098
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.007
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.011
GPT teacher head0.153
Teacher spread0.142 · 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
Published2019
Admission routes1
Has abstractyes

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