Quality of Parental Emotional Care and Calculated Risk for Coronary Heart Disease
Bibliographic record
Abstract
OBJECTIVE: To evaluate associations between perceived quality of parental emotional care and calculated 10-year risk for coronary heart disease (CHD). Little is understood about the role of parental emotional care in contributing to the risk for CHD. METHODS: The study sample was composed of 267 participants from the New England Family Study. Quality of parental emotional care was measured, using a validated short version of the Parental Bonding Instrument (PBI) as the average care scores for both parents (range = 0-12), with higher scores indicating greater care. Ten-year CHD risk was calculated, using the validated Framingham Risk Algorithm that incorporates the following prevalent CHD risk factors: age, sex, diabetes, smoking, total cholesterol, high-density lipoprotein cholesterol, and blood pressure. Multiple linear regression assessed associations of PBI with calculated CHD risk after adjusting for childhood socioeconomic status, depressive symptomatology, educational attainment, and body mass index. RESULTS: Among females, a 1-unit increase in the parental emotional care score resulted in a 4.6% (p = .004) decrease in the 10-year CHD risk score, after adjusting for covariates. There was no association between parental emotional care score and calculated CHD risk score in males (p = .22). CONCLUSION: Quality of parental emotional care was inversely associated with calculated 10-year CHD risk in females, and not males. Although the gender differences need further investigation and these findings require replication, these results suggest that the early childhood psychosocial environment may confer risk for CHD in adulthood.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".