Gamification to Engage Clinicians in Registering Data: A Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of additional gamification elements in a web-based registry system in terms of engagement and involvement to register outcome data, and to determine if gamification elements have any effect on clinical outcomes. METHODS: Randomized controlled trial for gynecologists to register their performed laparoscopic hysterectomies (LH) in an online application. Gynecologists were randomized for two types of registries. Both groups received access to the online application; after registering a procedure, direct individual feedback on surgical outcomes was provided by showing three proficiency graphs. In the intervention group, additionally gamification elements were shown. These gamification elements consisted of points and achievements that could be earned and insight in monthly collective scores. All gamification elements were based on positive enforcement. RESULTS: A total of 71 gynecologists were randomized and entered a total of 1833 LH procedures. No significant difference was found between the groups in terms of engagement and involvement on a 5-point Likert scale, respectively 2.34±0.87 versus 2.56±1.05 and 3.63±0.57 versus 3.33±1.03 for the intervention versus the control group (p>0.05). The intervention group showed longer operative time than the control group (108±42 vs. 101±34 minutes, p=0.04), no other differences were found in terms of surgical outcomes. CONCLUSIONS: The addition of gamification elements in a registry system did not enhance the engagement and involvement of clinicians to register their clinical data. Based on our results, we advise that registry systems for clinical data should be as simple as possible with the focus on the main goal of the registry.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".