Strength-Based Goal Setting Allows Veterinary Students to Reframe Their Neurodivergent Traits as Strengths: An Interpretative Phenomenological Analysis
Bibliographic record
Abstract
Many neurodivergent (ND) veterinary students report concerns regarding stigma, reluctance to disclose their difficulties to staff or peers, and poor experiences on placements. The limited support for these students aims to reduce disadvantages instead of recognizing potential advantages arising from neurodiversity. Character strengths identification, use, and development have been shown to increase the well-being of ND people, and strength-based goal setting (SBGS) allows for an asset-based approach to planning and achievement. However, SBGS has not been investigated in the context of ND veterinary medicine students. This study used semistructured interviews and interpretative phenomenological analysis (IPA) to investigate the following two research questions: (a) What is the lived experience of ND students in a veterinary medicine course? (b) What is the experience of participating in a strength-based workshop in a ND student-only environment? Out of 17 ND veterinary students who took part in the workshop, four participated in semistructured interviews to share their experience. IPA identified three group experiential themes with seven subthemes: (a) From outsider to in-group, (b) being ND is not a deficit but can be a strength, and (c) the positive impact of realistic structured goal setting. The ND-only workshop provided a "safe space" that allowed participants to "unmask" and reflect on their ND experiences. Identifying their signature strengths validated and reframed perceptions of ND traits. Rather than viewing neurodivergence as a weakness, these traits were viewed as overuse of a strength (e.g., rudeness as the overuse of honesty), allowing ND students the opportunity for self-regulation and control.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".