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Record W2883692516 · doi:10.1186/s40900-018-0110-6

Moving patient-oriented research forward: thoughts from the next generation of knowledge translation researchers

2018· article· en· W2883692516 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueResearch Involvement and Engagement · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of CalgaryDalhousie UniversityIzaak Walton Killam Health CentreCapital District Health Authority
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsKnowledge translationTheme (computing)Medical educationTranslational researchHealth careBest practiceMedicinePsychologyKnowledge managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

As knowledge translation trainee participants, we report on the discussions that took place during the 2017 Knowledge Translation Canada Summer Institute. The theme of the institute was patient-oriented research and patient engagement in research. Trying to move knowledge into health care practice can be difficult. Including patients and families as members of the research team can help to overcome some of these challenges by producing more relevant research designs and results. However, in the absence of guidelines and best practices, it can be difficult for trainees and researchers to effectively engage patients and families in designing and conducting research. We detail how trainees and early career researchers are currently engaging patients in their research, the strengths and challenges of engaging patients in research, and lessons learned. These discussions have helped us to identify important areas where future training and guidance is needed to support trainees as patient-oriented researchers. Background Moving knowledge into health care practice can present a number of challenges for researchers. Including patients and families as members of the research team can help to overcome some of these challenges by producing more relevant research designs and results. However, many trainees and researchers experience difficulty in engaging patients and families in research effectively. Main body We report on the discussions that took place at the 2017 Knowledge Translation (KT) Canada Summer Institute (KTCSI). The theme of the KTCSI was patient-oriented research and patient engagement in research. We provide an important viewpoint on how trainees and early career researchers are currently engaging patients in their research, the strengths and challenges of engaging patients in research, and lessons learned. As the target audience of the KTCSI, we provide our thoughts on what is needed to support trainees and researchers to more effectively engage patients and families in research. Conclusion While many of the participants at the KTCSI are conducting patient-oriented research, practical guidance, resources and tools are needed to ensure the effective engagement of patients in research. These discussions have helped us to identify how to move forward as patient-oriented researchers and where future work and support is needed to achieve effective engagement.

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.

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)0.025
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 score0.609
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.001
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 head0.866
GPT teacher head0.567
Teacher spread0.299 · 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