Instrumental Support and Its Impact on Psychological Capital and Well-Being in Online Learning: A Study of Hospitality and Tourism Students
Bibliographic record
Abstract
The COVID-19 pandemic’s influence on students’ mental health is significant, with online learning offering unique challenges and prospects. This study investigates the antecedents of student psychological well-being within this context, focusing particularly on instrumental support from instructors, students’ academic psychological capital (PsyCap), and school satisfaction. We surveyed Canadian tourism and hospitality students about their pandemic-era online learning experience, using Structural Equation Modeling (SEM) for data analysis. Our hypotheses were tested on a sample of 88 full-time students who had transitioned to online education, and our survey specifically asked about this online experience. Despite the small sample size, we utilized Partial Least Squares SEM (PLS-SEM), a technique well-suited for small sample sizes when using the SEM model, and confirmed the adequacy of our sample to ensure it met the minimum required sample size for PLS-SEM. Our findings reveal that instrumental support directly boosts students’ academic PsyCap—encompassing confidence, hope, optimism, and resilience. While instrumental support does not directly enhance school satisfaction, its total effect, mediated through academic PsyCap, is significant. Additionally, while instrumental support does not directly heighten psychological well-being, the mediation role of academic PsyCap is crucial. Our study thus underscores the importance of nurturing academic PsyCap to foster student satisfaction and well-being in digital learning environments. Furthermore, we validate that academic PsyCap influences both school satisfaction and psychological well-being. As such, universities should consider investing in programs that strengthen students’ psychological resources, ultimately enhancing their satisfaction and overall well-being, especially during online learning post-pandemic.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".