Patient oriented research in mental health: matching laboratory to life and beyond in Canada
Bibliographic record
Abstract
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.
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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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".