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Record W2947258860 · doi:10.1093/pch/pxz066.149

150 Cultivating Compassionate Care, Advocacy Skills and a Health Equity Lens in Resident Physicians: The Development of a Social Paediatrics Curriculum

2019· article· en· W2947258860 on OpenAlexaffabout
Jacqueline Ogilvie, Jill Sangha, Kaitlyn Bertman, Julie Gerber, Breanna Chen

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsCurriculumMedicineHealth careMedical educationFamily medicineLibrary sciencePsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Funded through an Associated Medical Services Phoenix Fellowship, this project is based in the Department of Paediatrics residency-training program. The overall aim is to advance compassionate care through a longitudinal curriculum focused on health equity, vulnerable populations and implicit bias. The objectives for the social paediatrics curriculum include: 1. Develop a richer understanding of the SDOH and application to paediatric practice 2. Develop advocacy skills for individual patients as well as the rights of children in the community and beyond 3. Enhance awareness of community programs 4. Examine one’s own unconscious bias 5. Develop clinical skills related to compassionate care. Social Paediatrics curriculum was researched and developed through the completion of a curriculum mapping exercise. This involved an environmental scan of current faculty teaching and mapping to Royal College requirements. Focus groups and consensus-building activities were held with faculty, residents, community experts, and patients/families. Through this work a pedagogical framework was established inclusive of: (1) academic teaching; (2) community experiences; (3) advocacy project; and (4) simulation learning. Learner assessment through a CBME-based entrustment tool has been developed with a focus on social medicine related skills. Built through faculty consensus building, curriculum mapping, and broad-based community engagement, this curriculum will be the first 3-year longitudinal implementation of Social Paediatrics learning in an Ontario residency-training program. Twenty four (24) residents and twenty nine (29) faculty members were engaged in developing curricular objectives, and over forty (40) community agencies and experts with learned and lived experience across the region have been partners in curriculum development and delivery. This method of curriculum delivery is unique as it is interdisciplinary in nature and longitudinal in scope. Residents learn from patients and families, community members, interdisciplinary professionals and academic experts alike. Additionally, models for resident assessment focus on professional development rather than the current model of remediation. Resident, faculty and community response to the program thus far has been overwhelmingly positive and a foundation has been laid for a sustainable, rigorous and diverse curriculum. New approaches to developing compassionate physicians, focusing on social justice and challenging biases are encouraged by this curriculum. Through community engagement and experiential activities, traditional physician-to-physician models of teaching and learning are enriched. Traditional structures and culture must be addressed in order to see this curriculum model realized. Research into the program’s efficacy and learner assessment models will guide medical education within and beyond this social paediatrics program.

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)0.006
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.382
Teacher spread0.351 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2019
Admission routes2
Has abstractyes

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