Writing and Reading Self-efficacy in Graduate Students: Implications for Psychological Well-being
Bibliographic record
Abstract
In an effort to identify critical antecedents of mental health challenges in graduate education, recent research has examined graduate students’ self-efficacy beliefs as a motivational antecedent of their productivity, persistence, and well-being. Whereas graduate students’ self-efficacy concerning their scholarly writing activities has received increasing research attention in regards to psychological health, the well-being implications of graduate students’ self-efficacy for academic reading remains underexplored. The present study assessed both writing and reading self-efficacy in an international sample of graduate students (N = 851) in relation to critical well-being indicators including exhaustion, engagement, quitting intentions, program satisfaction, and imposter syndrome. Hierarchical linear regressions revealed writing self-efficacy to be a strong predictor across well-being outcomes, with a significant two-way interaction highlighting the combined benefits of writing and reading self-efficacy for imposter syndrome in graduate students. The present findings are novel in highlighting the well-being implications of both writing and reading self-efficacy for graduate students and support the expansion of graduate education programs aimed at promoting writing competencies to also address reading-related issues.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".