Challenges in Masters level education: Supervision, stress and mental health
Bibliographic record
Abstract
Background and objective: In recent years, research has highlighted the prevalence of mental and emotional distress among graduate students. The objective of this manuscript-based PhD dissertation is to validate and iterate a conceptual model of Masters students mental health and wellbeing, with a focus on student expectations and the role of research supervision. Studies and findings: Manuscript #1, an integrative literature review, suggests a framework of four systems of graduate education that impact students’ emotional experiences: the culture and expectations of academia, the university and department, the lab and cohort systems, and the socio-economic system. Based on this framework, Manuscript #1 proposes a conceptual model for student experiences of mental and emotional distress and wellbeing that frames the research questions of the next studies. Manuscript #2 is a quantitative analysis focused on student expectations of graduate study. The results show that students whose expectations are aligned with their lived experiences of Masters study have less mental and emotional distress and greater wellbeing compared to students whose expectations do not match their lived experiences, and are less likely to consider withdrawing from their programs. Manuscript #3 is a qualitative analysis of Masters students’ descriptions of their graduate school expectations and experiences. Several themes are identified in the overall sample, including the importance of research and coursework, relationships, and academic culture. Students with aligned expectations in general reported more positive experiences of graduate school across all themes. The results also suggest an extension of the conceptual model presented in Manuscript #1, to include the idea that students perform a multi-factor cost-benefit analysis of their decision to enrol in their programs. Manuscript #4 is a mixed quantitative-qualitative study. Quantitative results show that students with greater satisfaction with supervision experience less mental and emotional distress and greater wellbeing compared to students who are dissatisfied. Qualitative results illustrate that satisfied students have positive experiences across different dimensions of supervision, whereas students who are not satisfied have equivocal or negative experiences across these dimensions. Discussion and conclusions: The dissertation findings support and extend the initial conceptual model. Several practical implications and recommendations 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".