Prioritizing Technology in Pharmacy Education: A Document Analysis of Strategic Plans
Bibliographic record
Abstract
The COVID-19 pandemic has required many pharmacy programs to increase their utilization of technology or shift the course of delivery entirely online. Delivery in this setting has exposed areas in the use of technology where pharmacy programs need to improve (such as staff and student training). This study performed a document analysis of strategic plans to identify technology-related strategies and where gaps in planning currently exist. Accredited pharmacy programs in Canada and the USA were included for analysis. A total of 77 strategic plans were identified. Strategic plans were searched for the phrases: "tech", "online", "distance" and "e-learning" to identify technology-related statements. Statements relating to technology in education were coded for (1) the prioritized "action" and (2) the objective or "goal" of this strategy. Quantitative analysis of these codes revealed that the "action" was most frequently to introduce or improve technology (54.4%), and the "goal" most frequently related to enhancing teaching/course delivery/learning (34.2%). Strategic plans appeared to frequently focus on the technology itself, with little consideration for the human aspect of operating technology or readiness of programs to embrace technology. Moving forward, strategic priorities with respect to technology should be refocused towards system readiness and account for resources necessary for target user upskilling and acceptance.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".