Consistency in adult reporting of adverse childhood experiences
Bibliographic record
Abstract
BACKGROUND: Many studies have used retrospective reports to assess the long-term consequences of early life stress. However, current individual characteristics and experiences may bias the recall of these reports. In particular, depressed mood may increase the likelihood of recall of negative experiences. The aim of the study was to assess whether specific factors are associated with consistency in the reporting of childhood adverse experiences. METHOD: The sample comprised 7466 adults from Canada's National Population Health Survey who had reported on seven childhood adverse experiences in 1994/1995 and 2006/2007. Logistic regression was used to explore differences between those who consistently reported adverse experiences and those whose reports were inconsistent. RESULTS: Among those retrospectively reporting on childhood traumatic experiences in 1994/1995 and 2006/2007, 39% were inconsistent in their reports of these experiences. The development of depression, increasing levels of psychological distress, as well as increasing work and chronic stress were associated with an increasing likelihood of reporting a childhood adverse experience in 2006/2007 that had not been previously reported. Increases in mastery were associated with reduced likelihood of new reporting of a childhood adverse experience in 2006/2007. The development of depression and increases in chronic stress and psychological distress were also associated with reduced likelihood of 'forgetting' a previously reported event. CONCLUSIONS: Concurrent mental health factors may influence the reporting of traumatic childhood experiences. Studies that use retrospective reporting to estimate associations between childhood adversity and adult outcomes associated with mental health may be biased.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".