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Record W2791669804 · doi:10.1177/1355819618762960

What constitutes meaningful engagement for patients and families as partners on research teams?

2018· article· en· W2791669804 on OpenAlexaffabout
Agnes Black, Kimberly Strain, Christine Wallsworth, Sara-Grey Charlton, Wilma Chang, Kate McNamee, Clayon B. Hamilton

Bibliographic record

VenueJournal of Health Services Research & Policy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsResearch CanadaUniversity of British ColumbiaProvidence Health Care
Fundersnot available
KeywordsThematic analysisQualitative researchFocus groupValue (mathematics)Health carePsychologyPublic relationsMedical educationNursingMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Objective There is growing emphasis on health care organizations to ensure that lay people are meaningfully engaged as partners on research teams. Our aim was to explore the perspectives of patients, family members and informal caregivers who have been involved on health care research teams in Canada and elicit their recommendations for meaningful engagement. Methods We conducted a qualitative study guided by thematic analysis of transcripts of focus groups and interviews of 19 experienced patient research partners in Canada. Results We identified four main themes: research environment, expectations, support and value, which highlight participants' combined perspectives on important factors to ensure their engagement in research is meaningful. Conclusions Our findings add to the evolving evidence base on the perspectives of lay people involved in health care research and their recommendations for research leaders on meaningful engagement. Our study suggests that research leaders should provide a welcoming research environment, outline appropriate expectations for patient research partners on research teams, support patient research partners' engagement in projects and recognize the value patient research partners bring to health research.

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)0.026
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.433
GPT teacher head0.637
Teacher spread0.204 · 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

Citations144
Published2018
Admission routes2
Has abstractyes

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