A scoping review and evaluation of instruments used to measure resilience among post-secondary students
Bibliographic record
Abstract
As mental health problems continue to increase among post-secondary populations, the need to develop effective initiatives designed to bolster students' resilience has increasingly been identified as a priority. Therefore, access to valid tools with which to measure the efficacy of these interventions is imperative. To date, a comprehensive assessment of existing instruments used to evaluate the construct of resilience among post-secondary student populations has not been conducted. The purpose of this study was to fill this gap by conducting a scoping review of literature detailing the use of resilience instruments and evaluating their quality based on suitability for use in the post-secondary setting and associated psychometric evidence. We identified a total of 78 records published between 2010 and 2022, extracting a total of 12 instruments. Using detailed criteria frameworks, each instrument was assessed in terms of suitability and quality of associated psychometric evidence for validity and reliability. The results of our study suggest that many of the instruments currently being used to assess resilience among post-secondary students may not be appropriate. The majority of the instruments included in our review were developed for use among general adult populations and not specifically designed for use in the post-secondary setting. Most instruments did not assess resilience in a comprehensive, holistic matter that addressed the ability to bounce back from adversity by drawing upon psychological, social, cultural, and environmental resources, as defined by recent research. Further, no instruments included in our review had published evidence in support of a complete psychometric analysis. The results of our evaluation suggest that the Connor-Davidson Resilience Scale (CD-RISC) is the most suitable instrument for measuring resilience among post-secondary populations due to its suitability, comprehensive assessment of the construct of resilience, and demonstrably strong psychometric properties for both the 25- and 10-item versions of the tool.
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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 | 0.056 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.042 | 0.038 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".