Mood and influenza vaccination in older adults: A randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: Positive mood on the day of vaccination has been associated with subsequent antibody responses to the influenza vaccine in older adults. The primary aim of this trial was to examine whether a brief intervention was able to enhance positive mood at the time of vaccination in a clinical context. Secondary aims included exploratory analyses of the effects of the intervention on nonspecific and influenza-specific immunity. METHOD: One hundred three older adults (65-85 years) participated in a 2-arm, parallel, single-blind, randomized controlled trial. Participants viewed either a 15-min video package designed to induce positive mood or a matched neutral control video, immediately prior to receiving a standard dose quadrivalent influenza vaccination. State affect and secretory immunoglobulin A levels were assessed immediately prior to, and following, the interventions. Antigen-specific immunoglobulin G responses to the vaccination were assessed at 4 and 16 weeks postvaccination. RESULTS: The positive mood intervention resulted in significant improvements in state positive affect, compared with the neutral control. Secretory immunoglobulin A levels significantly increased across both groups. Antigen-specific immunoglobulin G responses to influenza vaccination were not statistically significantly different between groups, although point estimates of effect size favored participants who viewed the positive mood intervention for most strains at both 4 and 16 weeks postvaccination. CONCLUSIONS: A 15-min intervention can improve positive mood in older adults prior to vaccination. Future trials should examine whether enhancing mood at the time of vaccination could enhance the effectiveness of influenza vaccination on patients and benefit health services. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".