The next generation of risk assessment and management
Bibliographic record
Abstract
Big data and analytics are rapidly changing health care and enabling a degree of measurement and quality improvement not previously seen. For a variety of reasons including the limited number of quality indicators in mental health care, psychiatry has been late to the game. Use of technology to measure, monitor, and assess risk and change, would have a significant impact for key stakeholders including patients, care providers, and the community. Analytics offer an opportunity to increase our understanding of the psychiatric populations, target effective programs and interventions, and direct more personalized care at the critical intersection of risk assessment and prediction – risk management. The electronic Hamilton Anatomy of Risk Management (eHARM) aims to harness the capabilities afforded by data analytics to enhance the assessment, monitoring, and management of risk at the clinical interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".