Assessment of the quality of the childhood physical abuse measure in the National Population Health Survey.
Bibliographic record
Abstract
BACKGROUND: The long-term health consequences of childhood physical abuse are often studied using retrospective self-reports collected from adults. This study assesses the quality of a question on childhood physical abuse in the National Population Health Survey (NPHS). DATA AND METHODS: All NPHS respondents aged 18 or older (n = 15,027) were asked a question about childhood physical abuse in cycles 1 (1994/1995), 7 (2006/2007) and 8 (2008/2009). The reliability of this question was assessed over these periods. Associations between response patterns to the abuse item and health conditions that are related to childhood physical abuse were examined. RESULTS: Across all NPHS cycles, very few respondents refused to answer or replied "don't know" to the item on childhood physical abuse. Reliability, as measured by Cohen's kappa statistic, was "substantial" for the two-year interval between cycles 7 and 8, and "moderate" for the 12- and 14-year intervals from cycle 1. Kappa estimates were similar when examined by various demographic factors. Compared with consistent deniers, respondents who consistently affirmed childhood physical abuse and those who provided inconsistent responses had increased odds of depression, fair or poor self-perceived health, disability, migraine, and heart disease. INTERPRETATION: Despite some limitations, the NPHS question on childhood physical abuse allows researchers to investigate long-term health consequences of abuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".