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Enregistrement W4410523366 · doi:10.1136/bmjoq-2025-qshu.55

55 Rainbow lessons: scaling an intervention to improve access to primary health care in Alberta, Canada

2025· article· en· W4410523366 sur OpenAlexaffabout
Myles Leslie

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésPrimary careRainbowIntervention (counseling)Computer scienceMedicineNursingFamily medicine

Résumé

récupéré en direct d'OpenAlex

Context Embedded in a Canadian provincial health system that is committed to delivering Primary Health Care (PHC) through teams of providers, the Crowfoot Village Family Practice (CVFP) is a full-service primary care clinic in Calgary, Alberta. CVFP is financed through an alternative relationship plan (ARP) that pays a salary, derived from blended capitation, to physicians.PROBLEM In 2023 CVFP had more than 4000 potential patients on its waiting list, each hoping to be attached to a clinic physician. ASSESSMENT: In an effort to reduce this waiting list and improve access to team-based PHC, CVFP medical leaders and staff at the clinic began creating a new care delivery model named Project Rainbow (Rainbow). ROOT CAUSE ANALYSIS: Rainbow’s designers began with the assumption that family physician time and availability were the major rate-limiting factors that were preventing patients from becoming attached to CVFP’s multi-disciplinary team of PHC providers. INTERVENTION: In a provincial system that generally equates physician access with PHC access, Rainbow innovated by making non-physician health care professionals the first point of contact for new patients. PATIENT INVOLVEMENT: A sub-set of patients on the waiting list were directly engaged about their willingness to participate in Rainbow, with a volunteer from that subgroup invited to serve on an advisory committee for the project. STRATEGY FOR CHANGE AND OUTCOMES: A robust data collection program shows the waiting list reduced significantly, as CVFP nurses – under the supervision of physicians – became first points of contact.This poster does not describe the specific interventions or the data collection activities that the CVFP team undertook in partnership with patients to ease the bottleneck and create a non-physician first point of contact. Both activities are ongoing and highly specific to CVFP’s finances, operations, and team structures. Instead, we draw out more broadly applicable lessons.We report on key facilitators of, and barriers to, Rainbow thriving in the clinic and scaling beyond its home ARP environment into the predominant fee-for-service financing of the provincial system. What follows is based on qualitative observation and interview data gathered by an embedded health services action researcher who worked alongside the CVPF team during the design and early implementation of Rainbow between August 2023 and February 2024. Understanding these facilitators and barriers is important to scaling Rainbow’s successes to meet the challenge of a nation-wide crisis in access to PHC.A FACILITATOR of Rainbow’s local success – one also aimed at enabling its spread beyond the CVFP – was the collection and use of data. Data were purposively collected to course-correct internally as well as to drive awareness and excitement about Rainbow in the external policy environment. Key BARRIERS to achieving local QI goals and scaling Rainbow beyond the clinic included workforce Human Resource (HR) issues, cultural/governance issues, and finance model issues.HR ISSUES It is unclear how existing efforts to improve physician recruitment and retention can be extended and leveraged to ensure not just family physicians, but the full range of PHC team members, are attracted to and sustainably integrated into programs like Rainbow. CULTURAL/GOVERNANCE ISSUES: Implementing Rainbow required the enactment of a culture of innovation and multi-disciplinary teamwork. Understanding how that culture and mental models that support novel distributions of professional authority and autonomy can be transmitted and supported with policy is central to achieving spread and scale. Amendments to scopes of practice, monopoly and competition frameworks, and learning environments that rewrite cultural norms by deconstructing hierarchical mental models to facilitate truly multi-disciplinary interaction require consideration. FINANCE MODEL ISSUES: Spreading CVFP’s ARP model is likely a necessary condition to enable scaling. Simply ‘fixing’ the finances, however, is unlikely to be sufficient. How to reform finances so that they support the resolution of workforce and cultural/governance issues remains an open question.KEY MESSAGES HR, Culture, and Finance issues are intertwined barriers to scaling a PHC access improving program in Alberta, Canada. More generally, embedded qualitative action researchers can help QI teams seeking to scale programs by co-identifying facilitators and barriers that go beyond the local.Conflicts of Interest This work was funded by the Alberta Innovates Health Solutions Fund, and the Canadian Institutes of Health Research. The authors declare no conflicts of interest.Ethics Approval Ethics approval was obtained from the University of Calgary Research Ethics Board (REB22-1385)The authors acknowledge that they have seen and agree to the license applied to conference abstracts published by BMJ.

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)0,003
score de la tête « metaresearch » (Gemma)0,005
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,076
Score d'incertitude au seuil0,550

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0090,002
Communication savante0,0020,001
Science ouverte0,0030,003
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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 Opus0,038
Tête enseignante GPT0,471
Écart entre enseignants0,432 · 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
Publié2025
Routes d'admission2
Résumé présentnon

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