Greater number of group identifications is associated with lower odds of being depressed: evidence from a Scottish community sample
Bibliographic record
Abstract
PURPOSE: Group identification has been shown to be associated with reduced risk of depression, but this research has important limitations. Our aim was to establish a robust link between group identification and depression whilst overcoming previous studies' shortcomings. METHODS: 1824 participants, recruited from General Practice throughout Scotland, completed a questionnaire measuring their identification with three groups (family, community, and a group of their choice), as well as their intensity of contact with each group. They also completed a self-rated depression measure and provided demographic information. Their medical records were also accessed to determine if they had been prescribed antidepressants in the previous 6 months. RESULTS: The number of group identifications was associated with both lower self-rated depression and lower odds of having received a prescription for antidepressants, even after controlling for the number of contact-intensive groups, level of education, gender, age, and relationship status. CONCLUSIONS: Identifying with multiple groups may help to protect individuals against depression. This highlights the potential importance of social prescriptions, where health professionals encourage a depressed patient to become a member of one or more groups with which the patient believes he/she would be likely to identify.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".