Conceptual Confusion: Identifying the Optimal Conceptualization of Resilience for Higher Education Students
Bibliographic record
Abstract
A rapidly burgeoning evidence base shows that low levels of resilience compromise higher education students’ well-being and academic success. Resilience programs can be an effective means of helping students adapt to the personal and academic stressors they may encounter. However, the development of such programs is hindered by inconsistent conceptualizations of resilience in the fields of psychology and education. To effectively support higher education students in strengthening their resilience, it is crucial to first clearly describe the construct in the literature, as its conceptualization lays the foundation for the development of ensuing resilience programs. This begs the question: Which conceptualization of resilience is most conducive to developing resilience programs within the context of higher education? The purpose of this paper is to discuss the diverse conceptualizations of resilience and identify the most appropriate one to underpin resilience programs in higher education. A review of the theoretical literature on resilience was conducted to achieve these two objectives. Upon examining trait, process, and outcome approaches to conceptualizing resilience, the process-oriented conceptualization is argued to be the most suitable for developing resilience programs in higher education. The adoption of a biopsychosocial approach to target the various factors that facilitate this resilience process is warranted to promote advancements in practice. In addition to having practical implications for professors, support staff, and policymakers involved in student well-being promotion efforts, these findings may help inform future resilience inquiries in psychology and education.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| 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".