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Record W2804011074 · doi:10.1093/pch/pxy054.137

HOUSING AND HEALTH: AN EXPLORATION OF HOUSING NEED IN CHILDREN WITH MEDICAL COMPLEXITY

2018· article· en· W2804011074 on OpenAlexaffabout
Kara Grace Hounsell, Julia Orkin, Eyal Cohen, Joanna Soscia, Kathy Netten, Arielle Zahavi

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPhoneSupportive housingPopulationDescriptive statisticsHousing FirstPsychologyHealth carePublic housingData collectionMedicineFamily medicineGerontologyEnvironmental healthMental healthPsychiatry

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Children with medical complexity (CMC), are defined by at least one chronic condition, functional limitation requiring technology support, multiple subspecialist involvement, and high healthcare utilization. A potentially significant social determinant that has yet to be examined in this population is that of “housing need.” According to the Canada Mortgage and Housing Corporation, “housing need” refers to housing that is crowded, unaffordable, or in need of major repairs. Given the known associations between housing and health, we hypothesize that housing need could be particularly relevant in CMC. Population-specific housing considerations may include in-home medical services, housing space, and disability accommodations. OBJECTIVES 1) Determine the prevalence of housing need in families with CMC and 2) Explore the experience and meaning of housing need of caregivers of CMC. DESIGN/METHODS We conducted a mixed methods study using questionnaires and semi-structured interviews. Recruitment occurred through a tertiary paediatric hospital. Research ethics board approval was obtained. All participants were English-speaking primary caregivers of CMC living in the same household. Questionnaires were completed in person, by phone, or online via REDCap, a secure data collection application. Questionnaires explored safety, affordability, and accommodations and were analyzed using descriptive statistics. Following the questionnaire, participants were invited to be interviewed in person or by phone. Interview questions were developed iteratively and were analyzed using grounded theory. RESULTS Of the 354 eligible caregivers, 93 participated in the questionnaire. A total of 36 caregivers (40%) reported some difficulties paying for housing each month. In addition, 56 participants (62%) described unmet need for accommodations (i.e. a lift, toilet accommodations, etc.). A total of 65 participants (83%) reported that their child’s condition(s) affected their preferred housing location, while 55 participants (70.5%) felt it affected their preferred housing type. Of the fifteen interviews completed, several themes emerged including limited housing options for families with CMC and financial, physical, and mental health consequences of housing challenges. In addition, caregivers felt counseling was lacking on 1) safe evacuation and 2) housing requirements, such as space and equipment, as children age. CONCLUSION This is the first in-depth exploration of housing need amongst families of CMC. Challenges identified include affordability, disability accommodations, and restrictions on housing type and location due to children’s illnesses. Clinicians can support families of CMC by offering counseling on evacuation planning and common housing requirements for 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)0.002
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.409
Teacher spread0.288 · 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
Published2018
Admission routes2
Has abstractyes

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