Use of the Smartphone App WhatsApp as an E-Learning Method for Medical Residents: Multicenter Controlled Randomized Trial
Bibliographic record
Abstract
BACKGROUND: The WhatsApp smartphone app is the most widely used instant messaging app in the world. Recent studies reported the use of WhatsApp for educational purposes, but there is no prospective study comparing WhatsApp's pedagogical effectiveness to that of any other teaching modality. OBJECTIVE: The main objective of this study was to measure the impact of a learning program via WhatsApp on clinical reasoning in medical residents. METHODS: This prospective, randomized, multicenter study was conducted among first- and second-year anesthesiology residents (offline recruitment) from four university hospitals in France. Residents were randomized in two groups of online teaching (WhatsApp and control). The WhatsApp group benefited from daily delivery of teaching documents on the WhatsApp app and a weekly clinical case supervised by a senior physician. In the control group, residents had access to the same documents via a traditional computer electronic learning (e-learning) platform. Medical reasoning was self-assessed online by a script concordance test (SCT; primary parameter), and medical knowledge was assessed using multiple-choice questions (MCQs). The residents also completed an online satisfaction questionnaire. RESULTS: In this study, 62 residents were randomized (32 to the WhatsApp group and 30 to the control group) and 22 residents in each group answered the online final evaluation. We found a difference between the WhatsApp and control groups for SCTs (60% [SD 9%] vs 68% [SD 11%]; P=.006) but no difference for MCQs (18/30 [SD 4] vs 16/30 [SD 4]; P=.22). Concerning satisfaction, there was a better global satisfaction rate in the WhatsApp group than in the control group (8/10 [interquartile range 8-9] vs 8/10 [interquartile range 8-8]; P=.049). CONCLUSIONS: Compared to traditional e-learning, the use of WhatsApp for teaching residents was associated with worse clinical reasoning despite better global appreciation. The use of WhatsApp probably contributes to the dispersion of attention linked to the use of the smartphone. The impact of smartphones on clinical reasoning should be studied further.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".