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Record W2802336760 · doi:10.1186/s40900-018-0096-0

What does patient engagement mean for Canadian National Transplant Research Program Researchers?

2018· article· en· W2802336760 on OpenAlexafffundabout
Julie Allard, Fabián Ballesteros, Samantha J. Anthony, Vincent Dumez, David Hartell, Greg Knoll, Linda Wright, Marie-Chantal Fortin

Bibliographic record

VenueResearch Involvement and Engagement · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsOttawa HospitalUniversité de MontréalCentre Hospitalier de l’Université de MontréalInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenUniversity of Toronto
FundersInstitute of Infection and ImmunityCanadian Institutes of Health Research
KeywordsOrgan donationDonationTransparency (behavior)MedicineTissue DonationMedical educationRelevance (law)TransplantationVariety (cybernetics)Qualitative researchPublic engagementPublic relationsPsychologyPolitical scienceSociologySurgery

Abstract

fetched live from OpenAlex

In recent years, the importance of involving patients in research has been increasingly recognized because it increases the relevance and quality of research, facilitates recruitment, enhances public trust and allows for more effective dissemination of results. The Canadian National Transplant Research Program (CNTRP) is an interdisciplinary research team looking at a variety of issues related to organ and tissue donation and transplantation. The aim of this study was to gather the perspectives of CNTRP researchers on engaging patients in research. We conducted interviews with 10 researchers who attended a national workshop on priority-setting in organ donation and transplant research. The researchers viewed patient engagement in research as necessary and important. They also considered that patients could be engaged at every step of the research process. Participants in this study identified scientific language, time, money, power imbalance, patient selection and risk of tokenism as potential barriers to patient engagement in research. Training, adequate resources and support from the institution were identified as facilitators of patient engagement. This study showed a positive attitude among researchers in the field of organ donation and transplantation. Further studies are needed to study the implementation and impact of patient engagement in research within the CNTRP. Background Involving patients in research has been acknowledged as a way to enhance the quality, relevance and transparency of medical research. No previous studies have looked at researchers’ perspectives on patient engagement (PE) in organ donation and transplant research in Canada. Objective The aim of this study was to gather the perspectives of Canadian National Transplant Research Program (CNTRP) researchers on PE in research. Methods We conducted semi-structured interviews with ten researchers who attended a national workshop on priority-setting in organ donation and transplant research. The interviews were digitally recorded and transcribed verbatim, and the transcripts were subjected to qualitative thematic and content analyses. Results The researchers viewed PE in research as necessary and important. PE was a method to incorporate the voice of the patient. They also considered that patients could be engaged at every step of the research process. The following were identified as the main barriers to PE in research: (i) scientific jargon; (ii) resources (time and money); (iii) tokenism; (iv) power imbalance; and (v) patient selection. Facilitating factors included (i) training for patients and researchers, (ii) adequate resources and (iii) institutional support. Conclusion This study revealed a favourable attitude and willingness among CNTRP researchers to engage and partner with patients in research. Further studies are needed to assess the implementation of PE strategy within the CNTRP and its impact.

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)0.055
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0550.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.743
GPT teacher head0.585
Teacher spread0.159 · 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

Citations18
Published2018
Admission routes3
Has abstractyes

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