Focused, Flourishing, but Not in Flow: Achievement Strivers’ Experiences of Competence, Flow, and Well-Being During Personally Expressive Activities
Bibliographic record
Abstract
Abstract One effective route to increasing well-being is through the pursuit of activities which suit a person’s personality strengths (i.e., person-activity fit). People who strive for achievement tend to organize their behaviors in ways that promote goal attainment and well-being. We tested the hypothesized process that achievement striving would lead to increased well-being over time through feelings of competence and flow. A secondary aim was to describe the types of personally valued activities and whether activity type facilitates competence and flow. Undergraduate students (N = 346 at Time 1; N = 244 at Time 2) completed an online survey measuring personality, personally expressive activities, basic psychological need satisfaction, flow, and well-being at two timepoints ~ 4 months apart. Two coders thematically coded activities into seven types (e.g., reading and writing, hobbies). We used cross-sectional and longitudinal serial mediation models to test our hypothesis with eudaimonic (life worth) and hedonic (life satisfaction) well-being, controlling for sample characteristics (recruitment source and term). Achievement striving was positively correlated to competence and well-being, but the indirect effects did not show that well-being is boosted by feeling competent and in flow during in personally expressive activities, cross-sectionally or longitudinally. Perceived competence was comparable across activity types, although flow was highest in reading and writing activities. While achievement strivers tended to feel happy and competent at personally expressive activities, the mechanistic pathway to well-being is not yet clear. Future studies might recruit larger sample sizes and utilize smaller time lags (e.g., ecological momentary assessment).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".