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Record W2894088909 · doi:10.9778/cmajo.20180034

The top research questions asked by people with lived depression experience in Alberta: a survey

2018· article· en· W2894088909 on OpenAlexafffundvenueabout
Lorraine Breault, Katherine Rittenbach, Kelly Hartle, Robbie Babins‐Wagner, Catherine de Beaudrap, Yamile Jasaui, Emily Ardell, Scot E. Purdon, Ashton Michael, Ginger Sullivan, A Unger, Lorin Vandall-Walker, Brad Necyk, Kiara Krawec, Elizabeth Manafò, Ping Mason-Lai

Bibliographic record

VenueCMAJ Open · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of CalgaryUniversity of AlbertaAthabasca UniversityAlberta Health Services
FundersCanadian Institutes of Health ResearchAlberta InnovatesAthabasca UniversityAlberta Health Services
KeywordsGeneral partnershipMental healthDepression (economics)AlliancePsychologyUnit (ring theory)MedicinePsychiatryNursingMedical educationGeographyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: To support patient-oriented setting of priorities for depression research in Alberta, the Patient Engagement Platform of the Alberta Strategy for Patient Oriented Research's Support for People and Patient-Oriented Research and Trials Unit and Alberta Health Services' Addiction and Mental Health Strategic Clinical Network, along with partners in addictions and mental health, designed the Alberta Depression Research Priority Setting Project. The aim of the project was to survey patients, caregivers and clinicians/researchers in Alberta about what they considered to be the most important unanswered questions about depression. METHODS: The project adapted the James Lind Alliance Priority Setting Partnership method into a 6-step process to gather and prioritize questions about depression posed by people with lived depression experience, which included patients, caregivers, clinicians and health care practitioners. RESULTS: Implementation of the project, from initial data collection to final priority setting, took 10 months (August 2016 to June 2017). A total of 445 Albertans with lived experience of depression participated, ultimately identifying 11 priority depression research questions spanning the health continuum, life stages, and treatment and prevention opportunities. INTERPRETATION: This project is a fundamental step that has the potential to positively influence depression research. Including the voices of Albertans with lived experience will create advantages for depression research for Albertans, researchers and research funders, and for patient engagement in the research enterprise overall.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.998

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 head0.447
GPT teacher head0.560
Teacher spread0.113 · 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

Citations14
Published2018
Admission routes4
Has abstractyes

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