MétaCan
Menu
Back to cohort
Record W3215323588 · doi:10.12927/hcpol.2021.26659

Comparing the Attainment of the Patient’s Medical Home Model across Regions in Three Canadian Provinces: A Cross-Sectional Study

2021· article· en· W3215323588 on OpenAlexafffundvenueabout
Sabrina T. Wong, Sharon Johnston, Fred Burge, Mehdi Ammi, John Campbell, Alan Katz, Ruth Martin‐Misener, Sandra Peterson, Manpreet Thandi, Jeannie Haggerty, William Hogg

Bibliographic record

VenueHealthcare policy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcGill UniversityVancouver Coastal HealthUniversity of ManitobaMcGill University Health CentreCarleton UniversityDalhousie UniversityUniversity of OttawaManitoba HealthInstitut du Savoir MontfortUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsPortraitPrimary careStrengths and weaknessesBaseline (sea)GeographyPsychologyRegional scienceMedicinePolitical scienceFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

Background: The aim of this work was to show the feasibility of providing a comprehensive portrait of regional primary care performance.Methods: The TRANSFORMATION study used a mixed-methods concurrent study design where we analyzed survey data and case studies.Data were collected in British Columbia, Ontario and Nova Scotia.Patient' s Medical Home (PMH) pillar scores were created by calculating mean clinic-level scores across regions.Scores and qualitative themes were compared.Results: Participation included 86 practices (n = 1,929 patients; n = 117 clinicians).Regions had differential attainment towards PMH orientation with respect to infrastructure; community adaptiveness and accountability; and patient and family partnered care.The lowest PMH attainment for all regions were observed in connected care; accessible care; measurement, continuous quality improvement and research; and training, education and continuing professional development.Conclusions: Comprehensive performance reporting that draws on multiple data sources in primary care is possible.Regional portraits highlighting many of the key pillars of a PMH approach to primary care show that despite differences in policy contexts, achieving a PMH remains elusive. RésuméContexte : L' objectif de ce travail est de montrer la faisabilité de brosser un portrait complet de la performance régionale des soins primaires.Méthode : L'étude de TRANSFORMATION a eu recours à des méthodes mixtes simultanées pour analyser les données d' enquête et les études de cas.Les données ont été recueillies en Colombie-Britannique, en Ontario et en Nouvelle-Écosse.Les scores du pilier des centres de médecine de famille (CMF) ont été obtenus en calculant les scores moyens cliniques dans toutes les régions.Les scores et les thèmes qualitatifs ont été comparés.Résultats : L'étude a porté sur 86 cliniques (n = 1 929 patients, n = 117 cliniciens).Les régions ont obtenu des résultats différents en matière d' orientation des CMF en ce qui concerne l'infrastructure, l' adaptabilité et la responsabilité communautaires, ainsi que les soins en partenariat avec le patient et la famille.Les résultats les plus bas des CMF pour toutes les régions ont été observés dans les soins connectés, les soins accessibles, les mesures, l' amélioration continue de la qualité et la recherche, ainsi que la formation, l'éducation et la formation professionnelle continue.Conclusions : Il est possible de produire un rapport de performance complet qui s' appuie sur plusieurs sources de données en soins primaires.Les portraits régionaux qui mettent en évidence bon nombre des piliers clés d' une approche des CMF en soins primaires montrent que, malgré les différences dans les contextes politiques, la réalisation d' un CMF reste insaisissable.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.150
GPT teacher head0.482
Teacher spread0.333 · 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

Citations9
Published2021
Admission routes4
Has abstractyes

Explore more

Same venueHealthcare policySame topicPrimary Care and Health OutcomesFrench-language works237,207