Do Our Friends and Relatives Help Us Better Assess Our Health? Examining the Role of Social Networks in the Correspondence Between Self-Rated Health and Having Metabolic Syndrome
Bibliographic record
Abstract
If individuals believe they are healthier than they actually are, they may feel less compelled to improve their health. This study aims to examine the importance of a person’s connections in the correspondence between a person’s self-reported health (SRH) and having metabolic syndrome (MetS). Participants of the Kingston Senior Women’s Study ( n = 100, 65 years of age and older) completed a questionnaire on their social background, psychosocial conditions, health behaviors, and health. Participants also provided physiological measures and medical information. Health overestimation was defined as reporting high SRH yet being diagnosed with MetS. Logistic regression was used to examine whether a person’s social networks increased the odds of health overestimation. About a third reported a high SRH and had MetS (36%), that is, overestimated their health. Participants had more than four social network ties on average, with a maximum of six reported ties. When control variables were accounted for, participants with larger network size had lower odds of health overestimation (odds ratio [OR] = 0.46, 95% confidence interval [CI] = [0.26, 0.80]). Women with larger social networks may have greater access to information about their own health, leading possibly to more accurate assessments. Such information may be conveyed via feedback from ties or via a more representative perception of what constitutes good health when self-assessing one’s health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".