Implicit measures of early-life family conditions: Relationships to psychosocial characteristics and cardiovascular disease risk in adulthood.
Bibliographic record
Abstract
OBJECTIVES: An implicit measure of early-life family conditions was created to help address potential biases in responses to self-reported questionnaires of early-life family environments. We investigated whether a computerized affect attribution paradigm designed to capture implicit, affective responses (anger, fear, warmth) regarding early-life family environments was (a) stable over time, (b) associated with self-reports of childhood family environments, (c) able to predict adult psychosocial profiles (perceived social support, heightened vigilance), and (d) able to predict adult cardiovascular risk (blood pressure) either alone or in conjunction with a measure of early-life socioeconomic status. METHOD: Two studies were conducted to examine reliability and validity of the affect attribution paradigm (Study 1, N = 94) and associated adult psychosocial outcomes and cardiovascular risk (Study 2, N = 122). RESULTS: Responses on the affect attribution paradigm showed significant correlations over a 6-month period, and were moderately associated with self-reports of childhood family environments. Greater attributed negative affect about early-life family conditions predicted lower levels of current perceived social support and heightened vigilance in adulthood. Attributed negative affect also interacted with early-life socioeconomic status (SES) to marginally predict resting systolic blood pressure (SBP), such that those individuals high in early-life SES but who had implicit negative affect attributed to early-life family conditions had SBP levels that were as high as individuals low in early-life SES. CONCLUSION: Implicit measures of early-life family conditions are a useful approach for assessing the psychosocial nature of early-life environments and linking them to adult psychosocial and physiological health profiles.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".