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Record W4410812167 · doi:10.1093/pch/pxaf022

An innovative approach to patient and family involvement in postgraduate medical education

2025· article· en· W4410812167 on OpenAlexaffabout
Sureka Pavalagantharajah, Muhammad Imran Khan, Clara Moore, Karen Beattie, Andrea Hunter, Elif Bilgiç, Bojana Babic

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcMaster Children's HospitalMcMaster University
Fundersnot available
KeywordsMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Although there is growing knowledge surrounding the value of involving patients and families in pediatrics residency training, based on a web-based survey of program directors (PDs) in Departments of Pediatrics across Canada, only 42% reported engaging patients and families in their formal residency curriculum. However, all respondents acknowledged the value of partnering with patients and families. Given this, we developed an educational intervention in partnership with our local Family Advisory Council members, that allows for (a) family voices to be heard and incorporated, and (b) direct interaction and conversation between families and pediatric residents. The intervention was divided into three sections: patient and family stories, a presentation about communicating effectively with parents, and a panel discussion. Overall, both family and resident feedback was overwhelmingly positive; hence, the success of our novel approach highlights the need to consider additional learning opportunities to actively involve patients and/or families within residency education.

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)0.008
metaresearch head score (Gemma)0.012
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0050.004
Scholarly communication0.0040.004
Open science0.0020.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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
Published2025
Admission routes2
Has abstractyes

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