MétaCan
Menu
Back to cohort
Record W4309410875 · doi:10.1177/23743735221138236

Exploring the Motivational Drivers of Young Adults with Diabetes for Participation in Kidney Research

2022· article· en· W4309410875 on OpenAlexafffund
P Mohini, M Palaganas, Yesmino Elia, Laura Motran, Etienne Sochett, J. Curtis, JW Scholey, Lynne McArthur, Farid H. Mahmud

Bibliographic record

VenueJournal of Patient Experience · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoUniversity Health NetworkHospital for Sick Children
FundersBreakthrough T1D CanadaCanadian Institutes of Health ResearchJuvenile Diabetes Research Foundation Canada
KeywordsCuriosityPsychologyAltruism (biology)Young adultCompensation (psychology)Financial compensationQualitative researchSocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Understanding motivational drivers and barriers to patient participation in diabetes research are important to ensure research is relevant and valuable. Young adults with type 1 diabetes (T1D) completed a 31-question qualitative survey evaluating participant experience, understanding, and motivators and barriers to research involvement. A total of 35 participants, 19-28 years of age, 60% female, completed the survey. Motivating factors included personal benefit, relationship with the study team, curiosity, financial compensation, altruism, and nostalgia. Older participants (>22 years) reported higher levels of trust in the study team (p = 0.02) and their relationship with the study team positively influenced their decision to participate (p = 0.03). Financial compensation was a strong motivator for participants with higher education (p = 0.02). Age, sex, education level, and trust in the study team influenced participants' understanding. Barriers included logistics and lack of familial support. Important motivational drivers and barriers to participation in research by young adults with T1D must be considered to increase research engagement and facilitate the discovery of new knowledge.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.405
GPT teacher head0.474
Teacher spread0.069 · 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 designObservational
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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Patient ExperienceSame topicMental Health and Patient InvolvementFrench-language works237,207