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Record 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 on OpenAlexaffabout
Myles Leslie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrimary careRainbowIntervention (counseling)Computer scienceMedicineNursingFamily medicine

Abstract

fetched live from 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.471
Teacher spread0.432 · 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
Published2025
Admission routes2
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