Exploring Experiences of Intense Academic Stretch in Veterinary Educators Enrolled in an Education Masters. Is This Imposter Syndrome or Am I a True Imposter?
Bibliographic record
Abstract
Professional work often requires individuals to operate outside their comfort zone (professional stretch), and this can facilitate learning and growth. Self-doubt is common when experiencing professional stretch. For some individuals in some contexts, self-doubt can be so profound that learning stops. Imposter syndrome (IS) is paradoxical self-doubt; individuals believe themselves poorly capable of competencies in which they excel. Affected individuals feel they must work exceptionally hard to succeed. IS has been observed in clinicians, students, and educators, which has important implications for supporting student development. This study sought to understand the relationship between academic/professional stretch and intense self-doubt in a population of veterinary educators, with the aim of better supporting learning in stretching workplace environments. Interviews were undertaken with seven female veterinary educators undertaking an MSc in Veterinary Education. Data were processed and experiential themes were derived using an interpretive phenomenological analysis (IPA) approach. Participants described extreme academic discomfort, linked to poor self-efficacy, exacerbated by feedback that was perceived to be unexpectedly negative. Distance followed by dialogue enabled some participants to process difficult emotions so that learning could continue. Participants recognized IS in others. Their own imposter-like thinking was less well recognized, being experienced in tandem with self-doubt rooted in real rather than imagined competence gaps. On the basis of this research, close mentoring relationships are advocated, so that through facilitated reflective dialogue, feelings of intense self-doubt can be better understood and supported, facilitating learning. Students can benefit from teachers' experiences of academic stretch, and this should be investigated further.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".