Pedagogical approaches to support student resilience in higher-education settings: A systematic literature review
Bibliographic record
Abstract
In recent years, Higher education (HE) students have continued to report rising rates of anxiety, depression and stress. One strategy employed to address these developments has been providing educational and administrative services that help to support and promote student resilience. Efforts to improve student resilience in HE may be bolstered by programs and strategies that go beyond traditional healthcare service delivery: for instance, initiatives such as in-course pedagogical approaches which target enhancing student resilience awareness and understanding. This systematic review aimed to identify, analyze, and synthesize the essential characteristics and programmatic features (e.g., methods) of pedagogical approaches (i.e., teaching strategies, curricula or other features) designed to support resilience among students in HE contexts. Searches were carried out in ERIC, PsychINFO, and SCOPUS and returned 1,545 results. Ultimately, thirty-five articles were included in the final synthesis. A three-level thematic analysis of the included thirty-five articles was conducted, in order to develop rigorous and consistent analytical themes. The five analytical themes that were subsequently developed included: 1) resilience education: reflection, understanding, awareness; 2) individual strategies: personal skill development; 3) institution- or department-level: structural, curricular opportunities; 4) interpersonal strategies: relational skill development; and 5) learning community: cohesion, integration, resource awareness. The implications and effectiveness of these themes for HE instructors are discussed.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".