Cumulative risk and protection measures data
Bibliographic record
Abstract
These data include clinically and theoretically grounded, cross-domain cumulative risk and protection measures. These measures were established for use with three sibling groups at Mothercraft's Breaking the Cycle (BTC), a child maltreatment prevention and early intervention program for substance using mothers and their children. These measures were established using archival data obtained from clients' charts. The cumulative risk factor measure encompasses: 1) items from a cumulative risk measure utilized in prior BTC research, 2) clinical measures assessing maternal mental health, addiction, and parenting capacity, 3) a measure utilized in studies on adverse childhood experiences, and 4) the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (Axis IV: Psychosocial Stressors) [1-3]. The cumulative protection factor measure encompasses: 1) existing early intervention components of services at BTC, 2) clinical measures assessing maternal mental health, addiction, and parenting capacity, and 3) known protective factors outlined in the literature. Both measures were theoretically grounded using the Developmental Model of Transgenerational Transmission of Psychopathology [4], which enabled salient domains of risk and protection to be delineated for children exposed prenatally to substances and accessing child maltreatment prevention and early intervention services. For a description of the process of establishing these measures, the total and cross-domain cumulative risk and protection percentages for the sample, as well as a qualitative interpretation of the balance between domains of risk and protection, see [5]. These measures can contribute to improved future understanding around cumulative risk and cumulative protection in vulnerable populations, salient domains of risk and protection, and the unique interaction that occurs between risk and protective processes in the context of child maltreatment prevention and early intervention.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.024 |
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".