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Record W4405330450 · doi:10.1177/23743735241302932

Patient Partner Perspectives: The Experience of Participating in a Co-Designed Virtual Reality Project

2024· article· en· W4405330450 on OpenAlexaff
Heather Thomson, Lisa Di Prospero, Sarah Xiao, Tamara Harth, Laurie Legere, Laura Rashleigh, Maria Parzanese

Bibliographic record

VenueJournal of Patient Experience · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsFeelingThematic analysisNarrativeCoronavirus disease 2019 (COVID-19)PsychologyMedical educationLived experienceNursingQualitative researchSociologyMedicineSocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Patient partners (PP) are well positioned to make meaningful contributions to healthcare through their lived experiences and personal narratives. However, researchers must ensure that patients are engaged authentically and collaboratively in knowledge generation. As part of a larger project, 4 PP were engaged in the co-design of a virtual reality video aimed at promoting an understanding of patients' lived experience with COVID-19 during the initial phase of the pandemic. This paper reports on findings from follow-up evaluation interviews with PP about their experiences participating in this project. Thematic analysis of interview transcripts resulted in 2 major themes as well as facilitators and barriers to PP engagement. Primary reasons to participate in the project were to contribute and give back to the community and make a difference for patients impacted by COVID-19. Engagement resulted in positive experiences and impacts for PP. Facilitators to engagement included feeling heard, being valued, and treated with respect. Barriers included length of time required to complete the project as well as PP health status.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.031
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.263
GPT teacher head0.510
Teacher spread0.246 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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