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Record W4366463831 · doi:10.1111/hex.13763

Co‐building a training programme to facilitate patient, family and community partnership on research grants: A patient‐oriented research project

2023· article· en· W4366463831 on OpenAlexafffundabout
Ingrid Nielssen, Sadia Ahmed, Sandra Zelinsky, Brian Dompe, Paul Fairie, Maria Santana

Bibliographic record

VenueHealth Expectations · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsGeneral partnershipTraining (meteorology)Community-based participatory researchMedical educationPsychologyMedicineNursingPolitical scienceSociologyParticipatory action researchGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: Patient engagement in patient-oriented research (POR) is described as patients collaborating as active and equal research team members (patient research partners [PRPs]) on the health research projects and activities that matter to them. The Canadian Institutes of Health Research (CIHR), Canada's federal funding agency for health research, asks that patients be included as partners early, often and at as many stages of the health research process as possible. The objective of this POR project was to co-build an interactive, hands-on training programme that could support PRPs in understanding the processes, logistics and roles of CIHR grant funding applications. We also conducted a patient engagement evaluation, capturing the experiences of the PRPs in co-building the training programme. METHODS: This multiphased POR study included a Working Group of seven PRPs with diverse health and health research experiences and two staff members from the Patient Engagement Team. Seven Working Group sessions were held over the 3-month period from June to August 2021. The Working Group worked synchronously (meeting weekly online via Zoom) as well as asynchronously. A patient engagement evaluation was conducted after the conclusion of the Working Group sessions using a validated survey and semi-structured interviews. Survey data were analysed descriptively and interview data were analysed thematically. RESULTS: The Working Group co-built and co-delivered the training programme about the CIHR grant application process for PRPs and researchers in five webinars and workshops. For the evaluation of patient engagement within the Working Group, five out of seven PRPs completed the survey and four participated in interviews. From the survey, most PRPs agreed/strongly agreed to having communication and supports to engage in the Working Group. The main themes identified from the interviews were working together-communication and supports; motivations for joining and staying; challenges to contributing; and impact of the Working Group. CONCLUSION: This training programme supports and builds capacity for PRPs to understand the grant application process and offers ways by which they can highlight the unique experience and contribution they can bring to each project. Our co-build process presents an example and highlights the need for inclusive approaches, flexibility and individual thinking and application. PATIENT OR PUBLIC CONTRIBUTION: The objective of this project was to identify the aspects of the CIHR grant funding application that were elemental to having PRPs join grant funding applications and subsequently funded projects, in more active and meaningful roles, and then to co-build a training programme that could support PRPs to do so. We used the CIHR SPOR Patient Engagement Framework, and included time and trust, in our patient engagement approaches to building a mutually respectful and reciprocal co-learning space. Our Working Group included seven PRPs who contributed to the development of a training programme. We suggest that our patient engagement and partnership approaches, or elements of, could serve as a useful resource for co-building more PRP-centred learning programmes and tools going forward.

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 categoriesMeta-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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0140.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.898
GPT teacher head0.631
Teacher spread0.267 · 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 designQualitative
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

Citations7
Published2023
Admission routes3
Has abstractyes

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