Students’ proposed self-management strategies in response to written cases depicting situations of adversity
Bibliographic record
Abstract
Introduction: Pharmacy students are facing academic and non-academic pressures that require emotional regulation. This study explored students’ possible self-management strategies when encountering situations known to deplete resilience. Methods: This was a qualitative think-aloud study designed to elicit final year pharmacy students’ reactions to situations known to deplete resilience and evoke emotional responses (racism, lack of trust, negative feedback, burnout, personal stress, sexual harassment). Thematic analysis was used to capture the strategies students used to self-manage their emotions. Results: Students made use of three types of processes to self-manage their emotions, which were used to construct three overarching strategies: the internalizer (avoidance, self-reflection), the seeker (asking for help or corroboration), and the confronter (approaching the situation and persons involved ‘head on’). Conclusion: Findings support the notion that students’ self-management is not a ‘one size fits all’ construct, and any approach to emotional skill development needs to recognize individualization within student responses.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".