The top research questions asked by people with lived depression experience in Alberta: a survey
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".