An Exploration of Resilience, Well-Being, and Authentic Leadership in Aspiring Student Leaders as they Transition from High School to University
Bibliographic record
Abstract
Before entering university, students are not always equipped with the tools to cope with stressful life events. During the transition to university, students may experience challenges that accompany this new stage of their life. However, research has shown that throughout this period, students can foster their strengths, resilience, and authentic leadership capabilities, supporting their well-being and transition to university. This study used a sequential explanatory mixed method design to examine the relationship between resilience, authentic leadership, and well-being through the transition from high school to university. The quantitative component of the research study followed the principles of Descriptive Correlation Design, and the qualitative component employed a Phenomenology approach. Participants were first-year university students at Queen’s University in Ontario, living both in and out of residence. Survey data, comprised of demographic questions, the Connor-Davidson Resilience Scale (CD-RISC), the Authentic Leadership Questionnaire (ALQ), and open-ended questions were gathered from 1012 participants. Fourteen participants that scored in the 75th percentile or higher on the CD-RISC participated in focus groups, and 10 of those individuals were also interviewed. Results indicate that resilience is significantly correlated to authentic leadership. A two-tailed bivariate correlation analysis found the CD-RISC25, the ALQ, and each of the subfactors of the ALQ statistically significant at the 0.01 level. A confirmatory factor analysis and a reliability analysis for the ALQ’s four factors were valid and reliable for students living both in and out of residence. Results indicated relationships between the four central constructs of the study: resilience, authentic leadership, well-being, and transition. The unifying theme, ‘Strength-Based Values for Authenticity’ emerged from the surveys, focus groups, and interviews. Analysis of findings yielded four major categories: challenges through the transition experience, foundations of growth, values-based strengths and characteristics, and readily available coping mechanisms. Data showed that highly resilient students were able to draw on their strengths more effectively through their transition to university, specifically when they were engaged in value-based leadership positions. Implications for practice include suggestions for educational wellness curricula; practices that support resilience, values-based leadership, and positive well-being; transitional support; and lessons learned from COVID-19’s impact on first-year students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".