MétaCan
Menu
Back to cohort
Record W4381434482 · doi:10.1177/23743735231183677

Patient-Partners as Educators: Vulnerability Related to Sharing of Lived Experience

2023· article· en· W4381434482 on OpenAlexaff
Kateryna Metersky, Rezwana Rahman, Jennifer Boyle

Bibliographic record

VenueJournal of Patient Experience · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsSession (web analytics)Vulnerability (computing)Communication sourcePsychologyPatient experienceMedical educationHealth communicationHealth carePublic relationsNursingMedicineBusinessPolitical scienceAdvertisingEngineering

Abstract

fetched live from OpenAlex

Patient-partners are invaluable in health professions’ education. Sharing their lived experiences with prospective and current healthcare providers can provide an opportunity for these participants to hone their patient-centric skills. However, sharing stories publicly is a vulnerable role and may feel emotionally risky for patient-partners. Using reflective dialogue, this manuscript outlines recommendations through the Sender-Receiver Model of Communication for Patient-Partners encounters when working with patient-partners in health professions’ education. These recommendations include recognizing that: Patient-partners need to consider if they are ready to share their story. Some stories are wounds requiring further healing; other stories are scars fully processed by patient-partners and ready to be shared publicly. The audience should differentiate between questions that can promote critical thinking versus feel like a “personal attack.” Audiences should recognize vulnerability patient-partners may experience in sharing their stories and engage accordingly. Pre-session and post-session debriefs are important. Shared stories may elicit intense emotions from patient-partners and audiences. Both groups should be given an opportunity to process and work through emotions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.401
Teacher spread0.355 · 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 teacher head, 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Patient ExperienceSame topicEmpathy and Medical EducationFrench-language works237,207