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Record W17289106 · doi:10.1093/pch/18.10.518

Children with medical complexity in Canada

2013· review· en· W17289106 on OpenAlexaffabout
Tammie Dewan, Eyal Cohen

Bibliographic record

VenuePaediatrics & Child Health · 2013
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsHealth careChronic diseaseMedicineChronic careDiseaseNursingPopulationMedical homeMedical careGerontologyFamily medicinePrimary careEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

The burden of chronic disease is placing pressure on the Canadian health care system. A small but important chronic disease population is children with medical complexity, defined as individuals with: high family-identified needs; complex chronic disease necessitating specialized care; functional disability; and high health care utilization. These patients present a challenge to community providers who are expected to provide holistic care and manage complex issues, often with a paucity of services and supports. Alternative models of care may address the complex needs of this population. In addition, strategies can be implemented in community practices that may assist with the care of children with medical complexity such as collaborative care, engagement of key workers, focus on goal-directed care and use of care plans. The paediatric community should engage in health care reform discussions focused on chronic disease to ensure that the complex needs of these children are met.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.102
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.094
GPT teacher head0.308
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations153
Published2013
Admission routes2
Has abstractyes

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