Digital literacy knowledge and needs of pharmacy staff: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To explore the digital literacy knowledge and needs of pharmacy staff including pharmacists, graduate (pre-registration) pharmacists, pharmacy technicians, dispensing assistants and medicine counter assistants. METHODS: A systematic review was conducted following a pre-published protocol. Two reviewers systematically performed the reproducible search, followed by independent screening of titles/abstracts then full papers, before critical appraisal and data extraction. Full articles matching the search terms were eligible for inclusion. Exclusions were recorded with reasons. Kirkpatrick's 4 level model of training evaluation (reaction, learning, behaviour and results) was applied as an analytical framework. RESULTS: Screening reduced the initial 86 papers to 5 for full review. Settings included hospital and community pharmacy plus education in Australia, Canada and the US. No studies of pharmacy staff other than pharmacists were identified. Main findings indicate that pharmacy staff lack digital literacy knowledge with minimal research evidenced at each level of Kirkpatrick's model. CONCLUSIONS: As a society, we acknowledge that technology is an important part of everyday life impacting on the efficiency and effectiveness of working practices but, in pharmacy, do we take cognisance, 'that technology can change the nature of work faster than people can change their skills'? It seems that pharmacy has embraced technology without recognised occupational standards, definition of baseline skills or related personal development plans. There is little evidence that digital literacy has been integrated into pharmacy staff training, which remains an under-researched area.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".