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Record W4294868414 · doi:10.1186/s40900-022-00381-7

The impact of patient engagement on trials and trialists in Ontario, Canada: An interview study with IMPACT awardees

2022· article· en· W4294868414 on OpenAlexafffundabout
Stuart G. Nicholls, Grace Fox, Zarah Monfaredi, Evelyn Poole, Chantelle Garritty, Alies Maybee, Justin Presseau, Beverley Shea, Dean Fergusson

Bibliographic record

VenueResearch Involvement and Engagement · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsPublic Health Agency of CanadaSimon Fraser UniversityUniversity of OttawaQueen's UniversityOttawa Hospital
FundersOntario Ministry of Health and Long-Term CareCanadian Institutes of Health ResearchOntario SPOR SUPPORT Unit
KeywordsThematic analysisPublic engagementPopulation healthPsychologyQualitative researchPatient experienceMedicinePopulationCommunity engagementMedical educationNursingHealth carePublic relationsPublic healthSociologyEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: A key component of patient-oriented research is the engagement of patients as partners in the design and conduct of health research. While there is now national infrastructure and networks to support the engagement of patients as partners, there remain calls for promising practices and success stories. In particular, there remains a keen interest in evaluating the impact that patient engagement has on health research studies. We aimed to investigate the impact that patient engagement had on health research conducted in Ontario, Canada. METHODS: Our sampling frame was studies that were awarded funding by the Ontario SPOR SUPPORT Unit. Semi-structured interviews were conducted with 10 principal investigators, members of research teams, and patient partners. Interviews explored the role of patient partners, the perceived impact of the patient engagement on the study, challenges faced, and advice for other researchers considering patient engagement. Data were analysed using the thematic analysis method with transcripts coded independently by two members of the study team. All coding and subsequent theme generation were discussed until consensus was achieved. RESULTS: There was variation in the methods used to engage patients and other stakeholders, the roles that patients and stakeholders occupied, and where they had input. Interviewees discussed two major areas of impact of patient engagement on research: impact on the study about which they were being interviewed, which tended to relate to improved relevancy of the research to the study population, and impact on themselves which led to changes in their own practice or approaches to future research. Identified challenges to patient engagement included: identifying and reaching patient advisors or patient partners, time-related challenges, and maintaining engagement over the course of the research. CONCLUSIONS: There remains a need to further build out the concept of relevancy and how it may be operationalised in practice. Further, the longer-term impacts of patient engagement on researchers and research teams remains under-explored and may reveal additional elements for evaluation. Challenges to patient engagement remain, including identifying and maintaining engagement with partners that reflect the diversity of the population of interest.

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)0.051
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.653
GPT teacher head0.554
Teacher spread0.099 · 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 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

Citations9
Published2022
Admission routes3
Has abstractyes

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