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Enregistrement 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 sur OpenAlexaboutno aff
Sara Long-Gagné

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSpecialtyMultidisciplinary approachHealth careKilometerMedical homePopulationAmbulatory careOutpatient clinicCohort study

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,162

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,004
Études des sciences et des technologies0,0020,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,052
Tête enseignante GPT0,304
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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