Personality and Subjective Well-Being: Towards Personalized Persuasive Interventions for Health and Well-Being
Bibliographic record
Abstract
Subjective well-being (SWB) is an individual’s judgment about their overall well-being. Research has shown that activities that elevate people’s sense of SWB have a significant effect on their overall health. There are two dimensions of SWB: Affective and Cognitive dimensions. However, studies on SWB usually focus more on one dimension, ignoring the other dimension. Also, most existing studies on SWB focused on individuals from Western cultures. Research has shown that the influence of personality on the subjective well-being components is moderated by culture. Thus, to advance research in personalizing persuasive health interventions, this study focuses on Africans (n=732). Specifically, we investigate the relationship between the Big-Five personality traits and both dimensions of SWB using the constructs: Happiness, Satisfaction with Life, Social, Psychological and Emotional well-being. Our results reveal that to design PTs to promote SWB for people high in Agreeableness, designers should focus on designing to promote their feeling of Happiness and Social Well-being, while for Neuroticism, designers should focus on designing to promote Psychological well-being and Emotional well-being. Based on our findings, we offer guidelines for tailoring persuasive health interventions to promote individuals’ SWB based on their personality.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".