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Record W3159790649 · doi:10.1186/s40900-021-00266-1

Patient oriented research in mental health: matching laboratory to life and beyond in Canada

2021· article· en· W3159790649 on OpenAlexaffabout
Jenessa N. Johnston, Lisa Ridgway, Sarah Cary-Barnard, Josh Allen, Carla L. Sánchez-Lafuente, Brady S. Reive, Lisa E. Kalynchuk, Héctor J. Caruncho

Bibliographic record

VenueResearch Involvement and Engagement · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsActive listeningMental healthPsychologyPopularityMedical educationStigma (botany)MedicinePsychotherapistPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

As patient-oriented research gains popularity in clinical research, the lack of patient input in foundational science grows more evident. Research has shown great utility in active partnerships between patient partners and scientists, yet many researchers are still hesitant about listening to the voices of those with lived experience guide and shape their experiments. Mental health has been a leading area for patient movements such as survivor-led research, however the stigma experienced by these patients creates difficulties not present in other health disciplines. The emergence of COVID-19 has also created unique circumstances that need to be addressed. Through this lens, we have taken experiences from our patient partners, students, and primary investigator to create recommendations for the better facilitation of patient-oriented research in foundational science in Canada. With these guidelines, from initial recruitment and leading to sustaining meaningful partnerships, we hope to encourage other researchers that patient-oriented research is necessary for the future of mental health research and foundational science.

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)0.015
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.385
GPT teacher head0.494
Teacher spread0.110 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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