Disentangling Vitality, Well-Being, and Quality of Life: A Conceptual Examination Emphasizing Their Similarities and Differences With Special Application in the Physical Activity Domain
Bibliographic record
Abstract
BACKGROUND: Given trends toward studying positive mental health in the behavioral sciences, the concepts of vitality, well-being, and quality of life (QoL) have received significant attention. Unfortunately, interpreting their empirical findings and applications is difficult given a tendency to use these terms synonymously and/or without clear apriori definitions. METHODS: This review presents an in-depth, critical examination of vitality, well-being, and QoL (especially health-related QoL) while paying particular attention to their similarities and differences. Given the proliferation of studies in the area of physical activity psychology, this review draws from a collection of knowledge in the physical activity domain to provide readers with concrete examples and to support arguments that are raised. RESULTS: The narrative content is divided into 3 sections with critical appraisals of each: definitions and meaning, theoretical views, and research, the latter of which is further subdivided into measurement and findings. Several parallels and discrepancies between the constructs are brought forward. CONCLUSIONS: Important arguments, among others, include the precision or specificity of the definition of vitality compared with well-being and QoL, and the emergence of a spectrum along which these constructs can be aligned with regards to the breadth of internal and external experiences they capture.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".