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Record W4294945977 · doi:10.1186/s40900-022-00384-4

Patient-identified priorities for successful partnerships in patient-oriented research

2022· letter· en· W4294945977 on OpenAlexafffundabout
Maria Santana, D.’Arcy Duquette, Paul Fairie, Ingrid Nielssen, Sumedh Bele, Sadia Ahmed, Tiffany Barbosa, Sandra Zelinsky

Bibliographic record

VenueResearch Involvement and Engagement · 2022
Typeletter
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCanadian Patient Safety InstituteUniversity of Calgary
FundersAlberta InnovatesUniversity of Calgary
KeywordsMedical educationPublic relationsBest practiceMandateCommunity engagementPsychologyMedicineNursingPolitical science

Abstract

fetched live from OpenAlex

Albertans4HealthResearch, supported by the Alberta Strategy for Patient-Oriented Research Patient Engagement Team, hosted a virtual round table discussion to develop a list of considerations for successful partnerships in patient-oriented research. The group, which consists of active patient partners across the Canadian province of Alberta and some research staff engaged in patient-oriented research, considered advice for academic researchers on how to best partner with patients and community members on health research projects. The group identified four main themes, aligned with the national strategy for patient-oriented research (SPOR) patient engagement framework, highlighting important considerations for researchers from the patient perspective, providing practical ways to implement SPOR's key principles: inclusiveness, support, mutual respect, and co-building. This commentary considers the process behind this engagement exercise and offers advice directly from active patient research partners on how to fulfill the operational patient engagement mandate. Academic research teams can use this guidance when considering how to work together with patient partners and community members.

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)0.029
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.383
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.017
Insufficient payload (model declined to judge)0.0030.000

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 head0.684
GPT teacher head0.532
Teacher spread0.151 · 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
GenreCommentary

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

Citations33
Published2022
Admission routes3
Has abstractyes

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