Happiness intervention decreases pain and depression, boosts happiness among primary care patients
Bibliographic record
Abstract
AIM: The aim of the study was to determine whether positive psychological interventions (PPIs) in a primary health care setting would improve physical and mental health over time. BACKGROUND: Most treatments for depression focus on reducing symptoms rather than on creating positive states of mental health. Empirical studies to verify the efficacy of PPIs in primary health care are needed. METHOD: In a six-week pilot program, we invited patients in a primary health care setting with symptoms of depression to participate in groups designed to increase levels of happiness. The program involved interventions such as engaging in good deeds, writing gratitude letters, and introducing empirical research. Patients completed the SF12v2(®) at the beginning and end of the program and at three- and six-month follow-up. Measures included physical functioning, bodily pain, mental health, social functioning, and vitality. Patients also participated in focus groups to discuss their experiences. FINDINGS: Of the 124 patients who enrolled in this pilot study, 75 completed the six-week program, and 35 participated in two follow-up assessments. Among the participants who remained for all follow-up assessments, scores improved from baseline to 6-month follow-up for health, vitality, mental health, and the effects of mental and physical health on daily activities. This subset of patients reported greater energy and more daily accomplishments, along with reductions in functional limitations. Improvements in mental and physical health and functioning were shown over a six-month period. The study provides a basis for the further investigation of PPIs in creating improvements for patients with depression in primary health care.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".