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Record W4386989232 · doi:10.1093/pch/pxad055.112

R1 (Resident Advocacy Project) Flexible and Enhanced Learning with our RICHER Partners: Reciprocal Partnerships between Medical Students and Equity-Deserving Communities to Promote Vaccine Access and Teach Health Advocacy

2023· article· en· W4386989232 on OpenAlexaboutno aff
Catherine Binda, Sanya Grover, Amy Beevor-Potts, L Bondi, Ethan Ponton, Will Lau, Alesia Dicicco, Noah Boroditsky, Lisa Ritland, Alysha McFadden, Gwynth McIntosh, Damian Duffy, Kate Hodgson, Lorelei Hawkins, Christine Loock, Taylor Rici, Lisa Szostek, Melody Tsai

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipPublic relationsService-learningEquity (law)Health equityCommunity engagementPolitical scienceMedical educationHealth careBusinessPsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

Abstract Rationale and Objectives The University of British Columbia provides medical students in years 1, 2, and 4 with protected time to explore the CanMEDs competencies in student-directed service, education, and research through the Flexible and Enhanced Learning (FLEX) program. Six FLEX students in the past three years have engaged in reciprocal partnerships with the Responsive, Intersectoral Community Child Health Education and Research group (RICHER). RICHER is a consortium of interdisciplinary healthcare providers, resource centres, and community members working across sectors and systems who serve equity-deserving children, youth, and families in Vancouver’s inner-city. Students develop leadership, health advocacy, and collaboration competencies through mentorship and co-learning with the RICHER team and community partners. For example, students have partnered to promote and amplify vaccine equity, knowledge of children’s and youth’s rights, food security, and social-emotional learning. Project Description For each FLEX project, community members identify local priorities. They conduct needs assessments and environmental scans. Students complete project proposals, ethics board and grant applications, and elicit iterative community feedback. Students collect data and communicate findings to stakeholders, including community members and RICHER clinicians. RICHER further amplifies community voices by sharing FLEX project results with local and provincial decision-makers, resulting in policy change and resource mobilization. Projects are evaluated qualitatively by stakeholders. For example, during the COVID-19 lockdown, many inner-city families faced barriers accessing COVID vaccines. The Downtown Eastside neighbourhood had some of the lowest vaccination rates in the province. Outcomes FLEX students surveyed 122 community members and found that 89 (73%) community members experienced a significant barrier to accessing COVID vaccines, such as: no access to technology or ID required to register or book an appointment, no access to transportation to a vaccine clinic, or trauma associated with accessing healthcare or injections. The RICHER team was able to respond to these findings by authoring a letter to the local health authority, who mobilized a pop-up vaccine clinic at an accessible, local community centre using the principles of brokered trust. Discussion/Future Directions This vaccine equity project has set a precedence for accessible care and created a model for future vaccine clinics to provide protection against viruses like influenza. FLEX students and hospital staff learned from Indigenous Elders and community members how to use the medicine wheel in the context of wholistic healthcare. As health advocates, FLEX students learned to ask, “who are we not seeing and why?” and learned how to write motivational letters to changemakers.

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)0.019
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.003
Scholarly communication0.0050.002
Open science0.0030.016
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0360.007

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 head0.164
GPT teacher head0.499
Teacher spread0.335 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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