A randomized controlled trial to examine the impacts of disclosing personalized depression risk information on the outcomes of individuals who are at high risk of developing major depression: a research protocol
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder is one of the most prevalent and disabling forms of mental illness in the general population. One public health strategy that may reduce the disease burden is early identification and prevention - identifying people who are at high risk and intervening to prevent symptoms from progressing into a major depressive episode (MDE). Multivariable risk predictive algorithms (MVRP) have been developed to estimate personalized risk (probability) of an MDE. The purpose of this trial is to answer the questions: (1) Does disclosure of personalized depression risk information promote high-risk individuals to take preventive actions? (2) Will disclosure of personalized depression risk information negatively affect the mental health of those at high risk? METHODS: We are recruiting 350 high-risk men and 350 high-risk women across the country. Individuals are eligible, if they: (1) are 18 years or older, (2) have not had a depressive episode in the past two months, (3) are at high risk of MDE based on the MVRPs (predicted risk of 6.5% + for men and of 11.2% + for women), (4) can communicate in either English or French, and (5) agree to be contacted for follow-up interviews. The MVRPs were developed and validated using longitudinal data from over 10,000 Canadians across the country. Eligible participants are randomized into (1) the control group, and (2) the group receiving personalized depression risk information. The participants are assessed at baseline, 6 and 12 months regarding accuracy of risk perception, use of self-help strategies and changes in psychological distress and functioning. Qualitative interviews are conducted in sub-samples of the intervention groups to explore how the personalized information affects risk perception, self-help behaviors and mental health. DISCUSSION: MVRPs can be used for risk stratification and planning preventive actions. The personalized risk information produced by MVRPs may also empower users to actively engage in self-management. This trial will contribute to the knowledge base about the potential health benefits and psychological harms associated with the provision of personalized depression risk information that will inform future implementation and patient-physician communication in the clinical settings. TRIAL REGISTRATION: NCT02943876 . Date of trial registration: October 21st, 2016.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".