Enduring education and employment: Examining motivation and mechanisms of psychological resilience
Bibliographic record
Abstract
Resilience, the ability to bounce back from difficult events, is critical for an individual to negotiate stressors and adversity. Despite being widely studied, little is known about the processes involved in the development of resilience. The goal of the studies are to investigate the relationship between motivation orientation, emotional intelligence, cognitive appraisals, and psychological resilience. Two studies, using self-report questionnaires were conducted with employed young adults also enrolled in post-secondary studies (pre- and during the pandemic) to test the tenability of our proposed models. Study 1 and Study 2 showed that emotional intelligence and challenge appraisals were mediators of autonomous motivation and resilience. Study 2 revealed statistically significant differences in mean scores of autonomous motivation and emotional intelligence between non-pandemic students and pandemic students. Based on the findings, it is suggested that autonomous motivation, emotional intelligence, and challenge appraisals are important aptitudes for the development of resilience. Furthermore, findings suggest that social isolation caused by the pandemic may have affected levels of emotional intelligence. Ultimately, the research expands the literature on both self-determination theory and resilience by offering a unique multiple mediation model for predicting the development of resilience within the employed undergraduate population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".