The introduction of an early warning signs journal in an adolescent inpatient unit
Bibliographic record
Abstract
The article focuses on the role that early warning signs recognition has played in a Child and Adolescent inpatient setting in the form of a journal that is used to record a young persons personal journey. Recording early warning signs to prevent a future relapse has been viewed as positive from the young people who have utilized the journal. It is a practical framework to identify key symptoms. Early warning signs are considered to be one of the many tools utilized by specialist early intervention practitioners and other mental health professionals to assist young people how to recognize a deterioration in their mental state or if a relapse is indicated. This article focuses on the role that early warning signs recognition has played in a Child and Adolescent inpatient setting in the form of a journal that is used to record a young person's personal journey by recording early warning signs to prevent a future relapse. Feedback on the use of the journal has been positive from the young people who have utilized the journal and it has also been viewed as a useful and practical framework to identify key symptoms that may potentiate a relapse into illness.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".