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Record W2785719664 · doi:10.1177/0840470417741712

Engaging patients as partners in health research: Lessons from BC, Canada

2018· article· en· W2785719664 on OpenAlexaffabout
Bev Holmes, Stirling Bryan, Kendall Ho, Colleen McGavin

Bibliographic record

VenueHealthcare Management Forum · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of British ColumbiaMichael Smith Health Research BC
Fundersnot available
KeywordsCredibilityLegitimacyRelevance (law)TelehealthUnit (ring theory)Public relationsMedical educationNursingMedicineHealth carePsychologyPolitical scienceTelemedicinePolitics

Abstract

fetched live from OpenAlex

Canada is seeing increased interest in engaging patients in health research, recognizing the potential to improve its relevance and quality. The momentum is promising, but there may be a tendency to ignore the challenges inherent when lay people and professionals collaborate. We address some of these challenges as they relate to recruitment, training, and support for patients at the British Columbia (BC) Support for People and Patient-Oriented Research Unit, part of Canada's Strategy for Patient-Oriented Research. A retrospective review of a telehealth project demonstrates that, as well as the practical elements of recruitment, training, and support, attention must be paid to issues of credibility, legitimacy, and power when engaging patients. We propose that all patient-oriented research projects would benefit from using a similar framework to guide patient engagement planning and implementation, helping to anticipate and mitigate challenges from the outset. Projects would ideally also include the study of patient engagement methods, to add to this important body of 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.469
GPT teacher head0.549
Teacher spread0.080 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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