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Record W4366331246 · doi:10.17294/2330-0698.1975

Patients and Families as Partners in Patient-Oriented Research: How Should They Be Compensated?

2023· article· en· W4366331246 on OpenAlexafffund
Monika Novak‐Pavlic, Jan Willem Gorter, Michelle P Phoenix, Samantha Micsinszki, Kinga Pozniak, Lin Li, Linda Anh B. Nguyen, Alice Kelen Soper, Elaine Yuen Ling Kwok, Jael Bootsma, Francine Buchanan, Hanae Davis, Sandra Abdel Malek, Karen M van Meeteren, Peter Rosenbaum

Bibliographic record

VenueJournal of patient-centered research and reviews · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSickKids FoundationMcMaster UniversityUniversity Health NetworkMcMaster University Medical Centre
FundersMcMaster University
KeywordsGeneral partnershipCompensation (psychology)Quality (philosophy)Public relationsFace (sociological concept)Health carePsychologyNursingBusinessMedicineMedical educationSocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Patient and family engagement has become a widely accepted approach in health care research. We recognize that research conducted in partnership with people with relevant lived experience can substantially improve the quality of that research and lead to meaningful outcomes. Despite the benefits of patient-researcher collaboration, research teams sometimes face challenges in answering the questions of how patient and family research partners should be compensated, due to the limited guidance and lack of infrastructure for acknowledging partner contributions. In this paper, we present some of the resources that might help teams to navigate conversations about compensation with their patient and family partners and report how existing resources can be leveraged to compensate patient and family partners fairly and appropriately. We also present some of our first-hand experiences with patient and family compensation and offer suggestions for research leaders, agencies, and organizations so that the health care stakeholders can collectively move toward more equitable recognition of patient and family partners in research.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.676
GPT teacher head0.579
Teacher spread0.097 · 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.

Study designNot applicable
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

Citations34
Published2023
Admission routes2
Has abstractyes

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