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Record W3212272605 · doi:10.1177/17470161211059993

Equality and Equity in Compensating Patient Engagement in Research: A Plea for Exceptionalism

2021· article· en· W3212272605 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Ethics · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsPleaExceptionalismResearch ethicsEquity (law)Political scienceDoctrineCompensation (psychology)Interpretation (philosophy)NormativePublic relationsPsychologyLawSocial psychology

Abstract

fetched live from OpenAlex

Engaging citizens and patients in research has become a truism in many fields of health research. It is now seen as a laudable—if not compulsory—activity in research for yielding more impactful and meaningful citizen/patient outcomes and steering research in the right direction. Although this research approach is increasingly common and commendable, we recently encountered a major obstacle in obtaining an ethics certificate from an institutional review board (IRB) to conduct a study that places citizen/patient perspectives on equal footing with those of academic/policy experts. The obstacle was the interpretation of fairness in terms of compensation for research participation (i.e. honoraria). In terms of research ethics, this raised an important question: Should all types of participants be compensated equally, or should exceptions be made for citizen/patient participants? We argue that there are good reasons for exceptionalism and that clearer guidance on citizen/patient engagement in research should be embedded into research ethics doctrine.

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.

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)0.086
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0860.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.007
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 head0.978
GPT teacher head0.767
Teacher spread0.212 · 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