Doctoral student mentorship in social work education: a Canadian example
Bibliographic record
Abstract
Purpose This paper aims to present a thematic analysis investigating the experiences and reflections of doctoral students in social work at a Canadian university who were mentored in the development of teaching expertise, including course design, delivery and evaluation, by a senior faculty member. Recommendations to others who are considering engaging in doctoral student teaching mentorship are presented. Design/methodology/approach The paper examines the authors’ reflections on their experiences of doctoral student mentorship through their involvement in collaboratively designing, teaching and evaluating an online undergraduate course. The inquiry used a qualitative approach grounded in Schon’s concept of reflexive learning. Findings Based on the results of the thematic analysis of the mentees’ reflections, this paper presents the collaborative teaching mentorship model and discusses how receiving mentorship in teaching facilitated the mentees’ development as social work educators. Originality/value Although quality guidelines in social work education recommend that doctoral students should be adequately prepared for future teaching opportunities, there is limited discussion about doctoral student development as educators within the academic literature, especially from the perspective of doctoral students. There is also limited articulation of specific models of doctoral student mentorship in developing teaching expertise. The authors hope that sharing their reflections on their experiences and describing the collaborative teaching mentorship model will serve to deepen understandings and promote further exploration and development of doctoral student mentorship in teaching.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.047 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".