Thriving through adversity: The role of passion and emotions in the resilience process
Bibliographic record
Abstract
OBJECTIVES: Two cross-sectional (Studies 1, N = 283 and 2, N = 275) and one prospective (Study 3, N = 238) studies investigated the role of passion (for academia) and emotions in the process of resilience in the education domain and in life in general. METHOD: Participants were examined when facing a stressful situation related to their passion for academia (end-of-term exam period and a timed education task). RESULTS: All three studies showed that harmonious passion, through its positive relationship with positive emotions, was positively associated with high positive outcomes in the education domain (satisfaction with one's studies, subjective and objective performance in one's studies) and in life in general via the subjective evaluation of one's life and general health indicators (subjective vitality and fewer negative physical symptoms). On the other hand, obsessive passion was related to mixed effects on resilience. Specifically, obsessive passion related to low levels of functioning (Studies 1 and 3) and also hindered the positive outcomes (Studies 1-3) through its positive relationships with positive and negative emotions, respectively. CONCLUSIONS: In sum, under stress, harmonious passion facilitates high resilience across life domains, whereas obsessive passion yields low resilience across the life or no resilience at all.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".