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Record W4365453460 · doi:10.1186/s40814-023-01290-6

Co-designing and pilot testing an infographic to support patients/families through the REMAP-CAP consent process: a mixed-methods study protocol

2023· article· en· W4365453460 on OpenAlexafffund
Heather K. O’Grady, Zahra Bhimani, Sandra Dalziel, Barbara Dolanjski, Gyan Sandhu, Marlene Santos, Kathy Smith, Srinivas Murthy, John C. Marshall, Michelle E. Kho

Bibliographic record

VenuePilot and Feasibility Studies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of British ColumbiaSt. Joseph’s Healthcare HamiltonSt. Michael's HospitalMcMaster University
FundersOntario Ministry of Research, Innovation and ScienceCanadian Institutes of Health ResearchInnovative Medicines CanadaMcMaster University
KeywordsInfographicInformed consentProtocol (science)Randomized controlled trialClinical trialData collectionTest (biology)Computer scienceMedicineData miningAlternative medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Informed consent is critical to the ethical conduct of clinical research and requires understanding of a trial including its purpose, process, potential risks and benefits, and alternatives to participation. This can be challenging for complex trials, such as platform trials, and in high-stress environments, such as the intensive care unit (ICU). REMAP-CAP (randomized, embedded, multifactorial, adaptive platform trial for community-acquired pneumonia) is a platform trial which studies treatments for ICU patients with community-acquired pneumonia, including COVID-19. Patient/family partners (PFP) identified challenges during the REMAP-CAP consent process. METHODS: This is a patient-centred co-design study to refine and test an infographic to supplement current REMAP-CAP consent documents. Infographic prototypes were developed by patients, substitute decision-makers (SDMs), and researchers with lived experience in the ICU or with ICU research. We will apply a two-phase exploratory sequential, mixed-methods research design. In phase 1, we will conduct focus groups with ICU patients, SDMs, and research coordinators (RCs). We will use inductive content analysis to inform infographic refinement, to be pilot tested in phase 2. Phase 2 is a prospective study within a trial (SWAT) at ≤ 5 REMAP-CAP sites. We will collect self-reported data from patients/SDMs and RCs. The primary outcome is feasibility (eligible consent encounters, receipt of infographic, consent to follow-up, completion of follow-up surveys). Data will be integrated to understand if/how quantitative results build upon the qualitatively informed infographic. DISCUSSION: Phase 1 results will be used to co-design an infographic, directly informed by the perspectives of patients, SDMs, and RCs involved in ICU research consent discussions. Results from phase 2 will determine the feasibility of infographic implementation in REMAP-CAP consent encounters. These feasibility data will inform a larger SWAT to evaluate our consent infographic. If successful, use of a co-designed infographic to support REMAP-CAP consent documents may improve the experience of consent for patients, SDMs, and RCs. TRIAL REGISTRATION: The Northern Ireland Hub for Trials Methodology Research SWAT Repository (SWAT no. 176).

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)0.167
metaresearch head score (Gemma)0.185
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.167
Threshold uncertainty score0.881

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1670.185
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0050.004
Scholarly communication0.0050.004
Open science0.0040.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0270.006

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 head0.709
GPT teacher head0.610
Teacher spread0.100 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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 routes2
Has abstractyes

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