Effectiveness of social giving on the engagement of pharmacy professionals with a computer-based education platform: a pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Computer-based education is gaining popularity in healthcare professional development education due to ease of distribution and flexibility. However, there are concerns regarding user engagement. This pilot study aims to: 1) assess the feasibility and acceptability of a social reward and the corresponding study design; and 2) to provide preliminary data on the impact of social reward on user engagement. METHODS: A mixed method study combing a four-month pilot randomized controlled trial (RCT), surveys and interviews. The RCT was conducted using a computer-based education platform. Participants in the intervention group had access to a social reward feature, where they earned one meal for donation when completing a quiz with a passing score. Participants in the control group did not have access to this feature. Feasibility and acceptability of the social reward were assessed using surveys and telephone interviews. Feasibility of the RCT was assessed by participant recruitment and retention. User engagement was assessed by number of quizzes and modules completed. RESULTS: A total of 30 pharmacy professionals were recruited with 15 users in each arm. Participants reported high acceptability of the intervention. The total number of quizzes completed by the intervention group was significantly higher compared to the control group (n = 267 quizzes Vs. n = 97 quizzes; p-value 0.023). CONCLUSION: The study demonstrates the feasibility and acceptability of a web-based trial with pharmacy professionals and the social reward intervention. It also shows that the social reward can improve user engagement. A future definitive RCT will explore the sustainability of the intervention.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".