Quality of supervision: postgraduate dental research trainees’ perspectives
Bibliographic record
Abstract
INTRODUCTION: Supervision is a pillar in enhancing the student's learning environment throughout her/his higher education. Multiple studies qualify graduate supervision among the most important contributors to the successful completion of a higher education degree and to graduate students' positive academic experience. The aim of this study was to assess the views of graduate students enrolled in the Dental Sciences and Craniofacial Research Graduate Programs at McGill University (n = 64) regarding the quality of supervision they are receiving. METHODS AND MATERIALS: An online questionnaire composed of 22 open and closed-ended format items was used and covered five domains: student profile, supervisory relationship, conflict resolution, student progress/thesis writing and career development. Descriptive statistics, chi-square tests and interpretative qualitative analysis were used to evaluate students' perspectives. RESULTS: Fifty-nine students completed the survey (92.2%). The distribution of sample in regard to the graduate student level was almost identical (M.Sc. level n = 28, Ph.D. n = 31). Overall, most graduate students appeared satisfied with the supervision they received and had similar perspectives about the surveyed domains. There was one statistically significant difference (P < 0.05) between MSc and PhD students when asked if their supervisors aided them in career development outside the supervisory relationship, where 77.4% (n = 24) of doctoral students agreed as opposed to 21.4% (n = 12) of Masters' students. CONCLUSIONS: Our results showed that McGill graduate students appeared to be overall satisfied with the supervision they received. The main elements contributing to a positive supervision experience were support, guidance, availability and good communication between supervisees and supervisors.
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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 | 0.008 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".