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Record W7162009511 · doi:10.82308/41892

Travel distance and patterns of health care utilization among children with medical complexity in Quebec, Canada: a population-based cohort study

2017· dissertation· en· W7162009511 on OpenAlexaboutno aff
Sara Long-Gagné

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSpecialtyMultidisciplinary approachHealth careKilometerMedical homePopulationAmbulatory careOutpatient clinicCohort study

Abstract

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Introduction: Children with medical complexity (CMC) represent a patient population with a wide range of medical conditions. CMC have high care needs and therefore are also high users of health care services, some that could potentially be reduced with optimal outpatient care such as readmissions to hospital. Although primary care providers are essential in the care of CMC, they cannot realistically provide the full range of care required by CMC without the support of a multidisciplinary team of specialized healthcare professionals. Currently, the majority of specialized services are provided within pediatric tertiary care centres. Objectives and methodology: For children suffering from any chronic health problems, a travel distance of more than 80 kilometres from hospital has been shown to negatively affect family unit dynamics and increase family anxiety when caring for their child at home due to the disruption in routine associated with such travels for which families may have to dedicate a whole day or overnight stay in order to reach their destination. We expected that difficulties associated with prolonged travels would limit specialty follow-up in the early period following a hospital discharge for CMC living at a driving distance of 80 kilometres or more to a tertiary pediatric centre compared to those living closer. Considering a fair proportion of early issues that may arise in the early post-discharge period could be addressed with adequate outpatient expert care, CMC living farther would be at increased risk of readmission within 30 days. Our primary objective was to look at the association between driving distance to the closest pediatric tertiary care centre (less than 80 kilometres compared to 80 kilometres or more) and the pattern of health service utilization, including time to readmission within 30 days following an initial hospital admission, in children aged 2 to 18 years with different levels of medical complexity from the province of Quebec. We used a population-based cohort design with multiple datasets from the Régie de l'assurance maladie du Québec and a Cox proportional hazard model to determine associations with our primary outcome. Results: Overall, we found that CMC in Quebec represented 2.2% of the total population of children and that 24% of these children lived at a driving distance of 80 kilometres or more from a pediatric tertiary care centre. Compared to those living at a driving distance of less than 80 kilometres, CMC located at a driving distance of 80 kilometres or more had less outpatient visits to family physicians, pediatricians or specialists, but more emergency department visits and repeated hospital admissions—yet, no association was found for the risk of readmission within 30 days of an initial hospitalization. Conclusion: Although driving distance was not associated with the risk of readmission within 30 days, we found that CMC living at a distance of 80 kilometres or more to a pediatric tertiary care centre utilized an increased number of unplanned/unscheduled services such as emergency department visits and repeated hospital admissions compared to those living at a driving distance less than 80 kilometres from a pediatric tertiary care centres. Moreover, a third of all CMC had no primary care provider. As these differences are unlikely to be solely explained by geographical barriers such as driving distance, the next steps would be to further understand the facilitators and barriers for families and for primary care caring for CMC, in order to develop programs and infrastructure that may reduce readmissions as well as improve the quality of care 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)0.001
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 score0.022
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.052
GPT teacher head0.304
Teacher spread0.252 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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