Pain and Depressive Symptoms in Primary Care
Bibliographic record
Abstract
OBJECTIVES: Pain and its disruptive impact on daily life are common reasons that patients seek primary medical care. Pain contributes strongly to psychopathology, and pain and depressive symptoms are often comorbid in primary care patients. Not all those who experience pain develop depression, suggesting that the presence of individual-level characteristics, such as positive and negative affect, that may ameliorate or exacerbate this association. METHODS: We assessed the potential moderating role of positive and negative affect on the pain-depression linkage. In a sample of 101 rural, primary care patients, we administered the Brief Pain Inventory, NEO Personality Inventory-Revised positive and negative affect subclusters, and the Center for Epidemiology Scale for Depression. RESULTS: In moderation models, covarying age, sex, and ethnicity, we found that positive affect, but not negative affect, was a significant moderator of the relation between pain intensity and severity and depressive symptoms. DISCUSSION: The association between pain and depressive symptoms is attenuated when greater levels of positive affects are present. Therapeutic bolstering of positive affect in primary care patients experiencing pain may reduce the risk for depressive symptoms.
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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".