The Extended Modified Maltreatment Classification System (EMMCS): A validation study
Bibliographic record
Abstract
BACKGROUND: One way to measure maltreatment is to code the narratives in Child Protective Services (CPS) files on a standardized instrument. This serves to document dimensions of maltreatment-such as types/subtypes, severity, frequency and chronicity-difficult if not impossible to measure from administrative data. The instrument most commonly used for this purpose is the Modified Maltreatment Classification System (MMCS, English & Longscan, 1997). Few studies have focused on the psychometric properties of such instruments. OBJECTIVE: This study sought to present the Extended Modified Maltreatment Classification System (EMMCS) and inter-rater reliability and convergent validity data for this updated instrument. PARTICIPANTS AND SETTING: The convenience sample was composed of the CPS files of 240 children 0 to 17 years old reported to a single CPS agency in Québec (Canada) and for which the allegations were declared substantiated by CPS practitioner. METHODS: Written narratives were coded on two instruments: the EMMCS (two coders) and the form used in the Étude d'incidence québécoise sur les situations évaluées en protection de la jeunesse - short form (FEIQ-SF, one coder). Inter-rater reliability was assessed by calculating kappas and intra-class correlations for all variables generated by the EMMCS for two coders and convergent validity by measuring the relationship between the EMMCS maltreatment variables and: a) the FEIQ-SF maltreatment variables and b) the FEIQ-SF child's functioning problems on the FEIQ. RESULTS: Inter-rater agreement on nearly all EMMCS variables (97 %) proved excellent, and EMMCS maltreatment type variables showed good convergent validity with FEIQ-SF variables (narrow convergent validity) and with indicators of child's functioning problems (broad convergent validity). CONCLUSIONS: Overall, the results of our study indicate that the EMMCS possesses excellent inter-rater reliability and good convergent validity. These promising results underscore the potential of the EMMCS to become a reference in the field.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".