Leadership Self-Efficacy (LSE) in Doctoral Programs: Examining the Supervisors’ Lived Experiences in Canadian Universities
Bibliographic record
Abstract
In this article, we describe Leadership Self-Efficacy (LSE) in doctoral programs by examining the lived experiences and perspectives of doctoral supervisors.A phenomenological research design was used to interview 16 supervisors from Canadian universities across all disciplines, social sciences and humanities, the natural sciences and engineering, and health sciences.The findings revealed the interplay of five types of efficacy in this context: research-self-efficacy (RSE) that is related to supervisors; research-self-efficacy (RSE) that is related to students; leadership self-efficacy (LSE) that is related to supervisors' roles; student self-efficacy (SSE) that is related to students' role; and, collective efficacy (CE).The main type of efficacy that made the difference in the doctoral studies context and allowed supervisors to help their students achieve their milestones, while maintaining their mental health, was the supervisors' Leadership Self-Efficacy (LSE).Effective supervisors found techniques to enhance the level of their LSE, and to support their students and enhance their students' sense of efficacy.However, the findings also suggest that supervisors experienced challenges in their roles and were not sufficiently supported, which may adversely influence their LSE and, in turn, affect doctoral students' performance and wellbeing.Implications include addressing the LSE in the doctoral supervision context at the individual level, group level, and departmental/institutional level.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".