Experiences of Academic Stress and Coping Mechanisms in High-Achieving Students
Bibliographic record
Abstract
The primary objective of this study was to explore the experiences of academic stress and the coping mechanisms employed by high-achieving students. The research aimed to identify key sources of stress, understand how these students manage their stress, and evaluate the impact of stress on their academic performance and overall well-being. This qualitative study utilized semi-structured interviews to collect data from 21 high-achieving students at a prestigious university. Participants were selected based on their high academic performance and involvement in extracurricular activities. Data analysis was conducted using NVivo software, following a thematic approach to identify recurring themes and patterns. Theoretical saturation was achieved, ensuring comprehensive coverage of the participants' experiences. The study identified several key sources of academic stress, including high expectations, heavy workload, time management challenges, peer competition, and lack of resources. Coping mechanisms employed by students included effective time management strategies, seeking social support, engaging in self-care practices, obtaining professional help, and making academic adjustments. The impact of academic stress was profound, affecting students' mental and physical health, academic performance, social relationships, personal development, motivation, and sleep patterns. The findings align with existing literature, highlighting the complex nature of academic stress and the diverse coping strategies used by students. High-achieving students experience significant academic stress due to various sources, which can negatively impact their well-being and academic performance. However, effective coping mechanisms and support systems can mitigate these effects. Institutions should develop comprehensive support systems, provide adequate resources, and foster a supportive academic environment to help students manage stress. Further research is needed to explore the role of individual differences and to develop targeted interventions for reducing academic stress among high-achieving students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".