Assessing dynamic risk factors in institutional settings using STABLE-2007
Bibliographic record
Abstract
Assessing dynamic risk factors for persons who reside in an institution can be a challenge. Conceptualizing and scoring dynamic risk factors is more difficult when environments are restricted and opportunities for those being assessed to demonstrate changes in behaviour may be few and far between. Additionally, because dynamic risk measures rely partly on file information scoring is dependent on the training and backgrounds of the people who record information and their personal decisions as to what they consider important enough to include in records. This may mean that scoring under research conditions based only on file review does not reflect the reliability of the measure under clinical conditions. Despite these challenges the present paper argues that there is sufficient evidence to support the use of STABLE-2007 as a reliable and valid measure of dynamic risk factors in institutional settings under both clinical and research conditions. Tips are provided on how to conceptualize institutional behaviours in a manner relevant to dynamic risk factors and how to weigh historical versus more recent information. Finally, recommendations are made for implementing a thoughtful system of checks and balances relevant to the assessment process in institutional settings.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".