Evaluating Change in Student Pharmacists’ Familiarity, Attitudes, Comfort, and Knowledge as a Result of Integrating Digital Health Topics Into a Case Conference Series: Cohort Study
Bibliographic record
Abstract
BACKGROUND: The use of technology in health care, often referred to as digital health, has expanded rapidly because of the need to provide remote care during the COVID-19 pandemic. In light of this rapid boom, it is clear that health care professionals need to be trained in these technologies in order to provide high-level care. Despite the growing number of technologies used across health care, digital health is not a commonly taught topic in health care curricula. Several pharmacy organizations have called attention to the need to teach digital health to student pharmacists; however, there is currently no consensus on best methods to do so. OBJECTIVE: The objective of this study was to determine if there was a significant change in student pharmacist scores on the Digital Health Familiarity, Attitudes, Comfort, and Knowledge Scale (DH-FACKS) after exposure to digital health topics in a yearlong discussion-based case conference series. METHODS: Student pharmacists' initial comfort, attitudes, and knowledge were gathered by a baseline DH-FACKS score at the beginning of the fall semester. Digital health concepts were integrated into a number of cases in the case conference course series throughout the academic year. The DH-FACKS was administered again to students after completion of the spring semester. Results were matched, scored, and analyzed to assess any difference in DH-FACKS scores. RESULTS: A total of 91 of 373 students completed both the pre- and postsurvey (response rate of 24%). Using a scale from 1 to 10, the mean student-reported knowledge of digital health increased from 4.5 (SD 2.5) before intervention to 6.6 (SD 1.6) after intervention (P<.001) and the mean self-reported comfort increased from 4.7 (SD 2.5) before intervention to 6.7 (SD 1.8) after intervention (P<.001). There was a significant increase in scores for all 4 elements of the DH-FACKS. The mean familiarity scores increased from 11.6 (SD 3.7) to 15.8 (SD 2.2), out of a maximum of 20 (P<.001). The mean attitudes scores increased from 15.6 (SD 2.1) to 16.5 (SD 1.9), out of a maximum of 20 (P=.001). The mean comfort scores increased from 10.1 (SD 3.9) to 14.8 (SD 3.1), out of a maximum of 20 (P<.001). The mean knowledge scores increased from 9.9 (SD 3.4) to 12.8 (SD 3.9), out of a maximum of 20 (P<.001). CONCLUSIONS: Including digital health topics in a case conference series is an effective and approachable way of providing education on important digital health concepts to students. Students experienced an increase in familiarity, attitudes, comfort, and knowledge after the yearlong intervention. As case-based discussions are an important component of most pharmacy and other medical curricula, this method can be easily applied by other programs that wish to give their students practice applying their knowledge of digital health to complex case-based scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".