Exploring Canadian pharmacy students’ e-health literacy: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: While much has been described about technology use by digital natives in general, understanding of pharmacy student's knowledge and understanding of technology is lacking. OBJECTIVE: This study explores the current state of pharmacy students' self-rated digital health literacy in British Columbia, Canada, and seeks to identify future opportunities for technology training in pharmacy education and in practice. METHODS: year pharmacy students at the University of British Columbia. An additional interview was offered to consenting participants to further explore the use of technology in daily lives, pharmacy practicums, and implications on future pharmacy curricula. Both quantitative and qualitative thematic analysis was done of all data. RESULTS: year students (50%), were 25 years and younger (80%), and female (87%). Ranking of digital health literacy was lower than expected with participants stating they know what (87%), where (87%) and how to find (77%) health resources on the Internet. Even less students (77%) rated that they have the skills to evaluate the health resources that they find on the Internet and only 53% felt confident in using information from the Internet to make health decisions. Most students mentioned that they had limited technology related training at school and would like more training opportunities throughout their program and connect what they have learned at school to their practice. CONCLUSIONS: These results expose significant and surprising gaps in student understanding of technology despite modifications seen in the entry-to-practice PharmD curriculum. Regional differences and digital health literacy of practicing pharmacists are areas that require better understanding and hold significant impact as practice evolves.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".