Impact of preferred learning style on personal resilience strategies among pharmacy students during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: Using COVID-19 as the context, this study explored how differences in individual learning styles impacted personal resilience strategies among pharmacy students. This is a uniquely stressful period of time for many learners; pharmacy education has shifted predominantly to novel online forms of teaching, learning, and assessment, and traditional psycho-social support became difficult to access due to lock-down and quarantine requirements. Methods: Data were gathered throughout May and June 2020 via participant-observer, semi-structured interviews. Data analysis was performed using deductive analysis techniques, based on existing themes in resilience research. Results: A total of 21 pharmacy students were interviewed, the majority of whom had ‘Assimilator’ or ‘Converger’ dominant learning styles as classified by Austin’s Pharmacists’ Inventory of Learning Styles (PILS). Assimilators had a stronger sense of professional identity, practiced positive psychology, and utilised journaling as resilience strategies more frequently than Convergers. Convergers were found to be more self-efficacious and adaptable than Assimilators. Conclusions: Rather than providing ‘one-size-fits-all’ advice and programming to pharmacy students, there may be potential to improve resilience by incorporating tailored and specific strategies based on the dominant learning style of each individual student.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".