How you ask matters: evidence-based assessment connecting decentering, reappraisal, and self-reported wellbeing in a post-secondary sample
Bibliographic record
Abstract
Effective and efficient Wellbeing measurement is essential within the social sciences and public health. Wellbeing is described as a three-factor construct composed of Life Satisfaction, Positive Affect, and Negative Affect, yet there are few measurement models validated for the increasingly popular use of longitudinal, app-based assessment. We explored Wellbeing measurement in a postsecondary student sample, including two mechanistic indicators described in Mindfulness-to-Meaning Theory: Decentering and Positive Reappraisal. Across two studies, we compared and validated popular measurement models for each construct. The most parsimonious Wellbeing model indicated only a two-factor structure comprised of positive (e.g., happiness, life satisfaction, and flourishing) and negative dimensions (e.g., anger, sadness, and anxiety). A third study revealed that a three-factor structure for Wellbeing was only supported when sampling a greater diversity of positive emotions than the earlier studies. Furthermore, while the Mindfulness-to-Meaning pathway to Wellbeing was replicated, only some operationalizations of Decentering and Reappraisal accounted for variance in Wellbeing. Concrete recommendations for the longitudinal assessment are provided. This research contributes not only to our understanding of Wellbeing, but also informs its optimal assessment in longitudinal research such as clinical trials and experience sampling studies.
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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 | 0.037 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".