Factors Associated with Transition to Serious Mental Illness: Facteurs associés à la transition à une maladie mentale grave
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in early intervention and detection strategies for youth at-risk of developing a serious mental illness (SMI). Little is known about early factors that may be related to the later development of a SMI; thus, the aim of this study was to determine what clinical factors might relate to the development of in this study psychosis, bipolar disorder and severe or recurrent major depression in at-risk youth. METHOD: The sample consisted of 162 youth aged 12-26 years at different stages of risk. Thirty-one participants developed a SMI during the study. Those who made a transition were compared on a range of baseline clinical and functional measures with those who did not make the transition. A Cox regression model was used to assess the association between measures and later development of a SMI. RESULTS: Female sex, attenuated psychotic symptoms as assessed with the Scale of Psychosis-Risk Symptoms (SOPS) and ratings on the K-10 Distress Scale, were found to be significantly associated with the later transition to mental illness. Females were 2.77 times more likely to transition compared to males. For the SOPS and K-10 scales, there is a 14% increase in the transition rate relative to a one-scale increase in SOPS and a 7% increase in the transition rate relative to a one-point increase in the K-10. CONCLUSIONS: Results from these longitudinal data provide further insight into the specific clinical measures that may be pertinent in early detection of mental illnesses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".