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Record W3210250883 · doi:10.1093/pch/pxab061.016

22 Housing Need Amongst Children with Medical Complexity: A Cross-Sectional Descriptive Study of Three Populations

2021· article· en· W3210250883 on OpenAlexaff
Kayla Esser, Clara Moore, Kara Grace Hounsell, Adrienne L. Davis, Alia Sunderji, Rayzel Shulman, Eyal Cohen, Julia Orkin

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsCross-sectional studyMedicineOddsLogistic regressionEnvironmental healthBedroomOdds ratioGeography

Abstract

fetched live from OpenAlex

Abstract Primary Subject area Complex Care Background Children with medical complexity (CMC) have medical fragility, complex chronic disease necessitating specialized care, functional disability, and/or high technology dependence. Housing is an important social determinant of health, yet research on prevalence and types of housing need in CMC is limited. Housing need encompasses unstable (frequent moves), inaccessible (lack of ramps/lifts), inadequate (major repairs needed), unsuitable (not enough bedrooms), or unaffordable housing. Given the association between housing and health, housing need may be an important consideration when caring for CMC. Objectives The primary objective was to describe the prevalence of and factors related to housing need in CMC. The secondary objective was to compare housing need between CMC, children with one chronic condition (Type 1 diabetes; CT1D) and healthy children (HC) to understand the relationship between chronic conditions and housing need. Design/Methods This was a cross-sectional descriptive study. Housing affordability, adequacy, suitability, stability, and accessibility were evaluated through surveys administered to caregivers of CMC, CT1D, and HC at a tertiary-care paediatric hospital using convenience sampling. The association of binary outcomes of housing need between groups was analyzed using logistic regression models, adjusting for sociodemographic factors (income, education, employment, geography, immigration status). Results 453 caregivers participated (Table 1). Compared to caregivers of HC, caregivers of CMC had higher odds of reporting one or more moves in the last two years (1.3 times), having safety concerns (3 times), using a common area as a bedroom (5.2 times), and experiencing housing stress (3.2 times), after sociodemographic factors were adjusted for (Table 2). Families of CT1D also had elevated odds of some indicators of housing need compared to HC, although to a lesser extent than CMC. 62.2% of CMC indicated they had to reduce spending on basics in order to afford their rent/mortgage, compared to 35.9% of CT1D and 25.2% of HC. Nearly two-thirds of CMC (60.2%) reported a need for accessibility accommodations in their home. Of those who installed accommodations, 62.9% felt the installations were a financial burden (cost ranged from $800-$80,000). Conclusion Families of CMC had higher odds of reporting unstable, inadequate, unsuitable, and stressful housing compared to HC even after sociodemographic factors were accounted for, suggesting an association between complexity of child health conditions and housing need. Access to appropriate housing may improve the health of CMC. Health care providers can screen for housing need, become familiar with housing interventions, and advocate for improved resources to address housing need in CMC.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.089
GPT teacher head0.357
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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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