Measuring Adverse Childhood Experiences: Comparing Individual, Composite, Score‑based and Latent Profile‑based Scoring Schemas Among Gay, Bisexual, and Other Men Who Have Sex with Men
Bibliographic record
Abstract
Adverse childhood experiences (ACEs; e.g., neglect, sexual abuse) among gay, bisexual, and other men who have sex with men (GBM) may not occur in isolation, but may be connected and occur in clusters. Most studies have measured ACEs individually, hierarchically, additively, or in a binary fashion (presence or absence of ACEs), rather than treating them as connected and clustered. This study examined these competing approaches of scoring ACEs and their relative power at predicting health outcomes. We examined abuse (sexual, physical, and emotional) and neglect (physical and emotional) experiences among a non-random sample of 470 Toronto GBM using the Childhood Trauma Questionnaire Short Form subscales. We compared five scoring schemas: (1) five individual scores for each form of maltreatment; (2) a composite score summing all of the maltreatment scores; (3) a hierarchical regression model with sexual abuse entered first then followed by physical abuse, emotional abuse, physical neglect, and emotional neglect; (4) a severity-based categorization; and (5) a latent profile-based categorization. Experiences of abuse and neglect were not uncommon (22-33%) and some participants experienced multiple forms of abuse and neglect (r = .33-.65, df = 464-467; p < .001; shared variance, r2 = 11-43%). Results show the dose-response effects of ACEs and highlight the importance of examining ACEs in clusters rather than individually. Latent profile analysis identified GBM who experienced multiple and frequent ACEs, and also identified the types of ACEs they experienced: crucial information that was obscured in score-based or severity-based approaches.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".