A Self-Regulation Framework to Support the Mental Health and Wellbeing of International Female Graduate Students
Bibliographic record
Abstract
Managing stressors is a growing concern among international graduate students, particularly for international female graduate students (IFGS) during the global COVID-19 pandemic. IFGS have coped with delayed program starts, loss of economic stability, social isolation, uncertainty, and continuous readjustments over this period. Self-regulation is a framework for understanding and managing stress that can be beneficial in helping students with their learning, mental health, and wellbeing. This chapter explores the journey of an IFGS and shows how the Shanker Self-Reg framework can help across five interrelated domains (biological, emotional, cognitive, social, and pro-social), and how the 5-step Shanker Self-Reg framework can be applied to the unique stressors and challenges that an IFGS faced when transitioning to Ontario, Canada during the COVID-19 pandemic. Complementary narratives from the IFGS and her supervisors show how self-regulation requires continuous revisiting from establishing a supervisory relationship, designing a research plan, taking initial life steps after acceptance to the program, finding residence, selecting courses, and making progress throughout continuous uncertain situations. The impact of family responsibilities and academic demands on this IFGS coupled with the need to use available resources, including the support of supervisors, is explored. Finally, the authors advocate for a self-regulation framework approach for stress reduction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".