Collaborative Application of Resilience Education (CARE): Fostering Resilience to Combat Depression and Anxiety in Graduate Students
Bibliographic record
Abstract
Background: Graduate students experience depression and anxiety at rates six times higher than the general population. A literature review of resilience education and an environmental scan of existing preventative resources found that current university-provided mental health interventions are primarily reactive, short-term, and crisis-oriented. There remains a need for preventative resources that prepare students to effectively face adversity and maintain wellbeing during graduate school. Purpose: In collaboration with experts in mental health, psychology, pedagogy, resource development, and graduate students themselves, our team aims to develop and implement a proactive, skill-based resource that teaches strategies to foster resilience in graduate school while emphasizing the importance of self-care, self-awareness, and help-seeking. Methods: Our new co-created resource is to be assessed in three phases: (1) Qualitative Needs Analysis, (2) Intervention Ideation, and (3) Resource Validation. All participants for this study will be graduate students in the first two years of their degree and recruited from the University of Toronto Temerty Faculty of Medicine (TFM). Phase One will be conducted through qualitative focus groups to explore student needs and experiences with mental health resources. Focus group data will be transcribed and analyzed using NVIVO to establish criteria for our resource prototype. In Phase Two, all data, in addition to input from a student advisory committee composed of TFM graduate students, will be compiled to design our prototype. In Phase Three, focus groups will assess the prototype and provide their feedback on the content and functionality of the resource, and where it might be improved. Anticipated Results and Potential Implications: Overall, we anticipate that this resource prototype will leave a positive impression on the Phase Two participants, helping them build their resilience-based skills and strategies for facing adverse events. Overall, given the correlation between increased resilience and improved mental health, we expect that this resource will reduce levels of depression and anxiety for students by preparing them for the stress they experience in graduate school. If successful, this resource may be adapted to address the needs of graduate students in different departments, universities, and institutions worldwide.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".