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Record W2606654256 · doi:10.1177/1367493517702527

Profile of a national sample of Canadian children with participation and activity limitations

2017· article· en· W2606654256 on OpenAlexaffabout
Joshua Lawson, Donna Goodridge, Donna Rennie, Guangming Zhao, Darcy D. Marciniuk

Bibliographic record

VenueJournal of Child Health Care · 2017
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSample (material)PsychologyMedicineEnvironmental healthChemistry

Abstract

fetched live from OpenAlex

Little is known about the nature of Canadian children with participation or activity limitations. Our objective was to profile a nationally representative sample of Canadian children with report of participation or activity limitation including identifying the major medical reasons attributed to these limitations and describe their sociodemographic and functional characteristics. We used data from the Canadian 2006 Participation and Activity Limitation Survey, a post-census Statistics Canada national survey of adults and children whose everyday activities were limited because of a condition or health problem. Data were collected by telephone interview of children's (<15 years) parents. A sample of those who answered 'yes' to the 2006 Canada Census disability filter questions was chosen for follow-up. Functional ability was assessed using the Health Utility Index. Mental health (26.1%) was the most common reason reported for participation and activity limitations followed by respiratory (9.8%), neurological (5.5%), and congenital (4.6%) conditions. Having a comorbid condition was associated with each major reason for limitation. Mental health, neurological, and congenital conditions showed the highest risk of functional limitation. In conclusion, mental health conditions and those with multiple conditions should be a primary focus for interventions aimed at reducing the impact of health conditions.

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)0.000
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 score0.090
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.045
GPT teacher head0.338
Teacher spread0.293 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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