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Record W4386188106 · doi:10.1177/13558196231197288

A patienthood that transcends the patient: An analysis of patient research partners’ narratives of involvement in a Canadian arthritis patient advisory board

2023· article· en· W4386188106 on OpenAlexafffundabout
Graham Macdonald, Jenny Leese, Alison M. Hoens, Sheila Kerr, Wendy Lum, Lianne Gulka, Laura Nimmon, Linda Li

Bibliographic record

VenueJournal of Health Services Research & Policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsResearch CanadaUniversity of OttawaUniversity of British Columbia
FundersCanadian Institutes of Health ResearchVancouver Foundation
KeywordsNarrativeMedicineArthritisDemocracyNarrative inquiryPublic relationsPsychologyFamily medicineSociologyPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

OBJECTIVES: Incorporating the perspectives of patients and public into the conduct of research has the potential to make scientific research more democratic. This paper explores how being a patient partner on an arthritis patient advisory board shapes the patienthood of a person living with arthritis. METHODS: An analysis was undertaken of the narratives of 22 patient research partners interviewed about their experiences on the Arthritis Patient Advisory Board (APAB), based in Vancouver, Canada. RESULTS: Participants' motivations to become involved in APAB stemmed largely from their desire to change their relationship with their condition. APAB was a living collective project in which participants invested their hope, both for their own lives as patients and for others with the disease. CONCLUSIONS: Our findings highlight how the journeys of patient partners connect and integrate seemingly disparate conceptions of what it means to be a patient. One's experience as a clinical 'patient' transforms into the broader notion of civic patienthood.

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)0.029
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0300.027
Scholarly communication0.0140.009
Open science0.0040.017
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0040.000

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 head0.317
GPT teacher head0.545
Teacher spread0.229 · 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.

Study designQualitative
DomainMethods
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
Published2023
Admission routes3
Has abstractyes

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