Bibliographic record
Abstract
“The I in illness is isolation, and the crucial letters in wellness are we”.1 Social relationships, or the lack thereof, constitute a major psychosocial risk factor for health, rivaling the effect of well-established traditional cardiac risk factors.2 For example, the INTERHEART study, a case-control study of patients across 52 countries who had experienced an acute myocardial infarction, reported that adverse psychosocial factors accounted for a population attributable risk of 32.5%, which is similar to the population attributable risk for smoking of 35.7%.3 Social integration has been defined as the presence of social relationships that provide a sense of belonging, a subjective bond that individuals feel in relation to others and groups of others.4 A network of positive relationships can provide a tremendous source of support, meaning, and belonging,5 whereas the absence of relationships or a state of social isolation can have detrimental implications for health trajectories and well-being. Loneliness—the perception that one's desired social relationships or connectivity are not being fulfilled—has been identified as a significant risk factor for depression and poor health behaviours.6 Although the prevalence of loneliness can vary with age or life stage, being married or with a partner does not necessarily ensure protection from loneliness. Poor marital quality or dissatisfaction has been associated with higher levels of reported loneliness, new onset depression, and poor long-term survival.7–11 The Framingham Offspring Study further documented that repressed marital communication, conflict, and strain were all associated with adverse health outcomes, especially in women.12 These data strongly suggest that psychosocial factors such as loneliness and marital quality, a component of social integration, exert considerable influence on the biopsychosocial experience of recovery and resulting health outcomes.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".