Demographic and Attitudinal Factors Influencing Doctoral Student Satisfaction
Bibliographic record
Abstract
Higher education administrators face challenges in providing a welcoming environment for doctoral students in higher education institutions, as they must identify factors influencing students’ satisfaction in order to provide a supportive environment, reduce attrition rates, and promote persistence. Thus, the purpose of this study was to identify predictors of doctoral student satisfaction from demographics and attitudes concerning the campus environment. Participants were 132 (33 male, 99 female) doctoral students from two private nonprofit universities in the New York metropolitan area of the United States who completed either a web-based or paper/pencil survey in which demographics and opinions regarding student satisfaction were sought. Regression analysis on participant attitudes found that university services, advisor, and students were all significant predictor variables. Other demographic predictor variables included years in graduate school, race, and ethnicity. Of particular importance, as doctoral students progress in their program by year, dissatisfaction increases. This could be due to the increasing pressures of successfully completing the dissertation, the progress of which can be heavily influenced by advisor-student relationship. Overall findings may assist education administrators and institutional planners in making campus environments welcoming to students thereby increasing both student satisfaction and retention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".