Smart-phone and medical app use amongst Irish medical students: a survey of use and attitudes
Bibliographic record
Abstract
Studies in the UK and Canada reveal high smart-phone ownership rates with the majority of students viewing these devices as very useful with regards to their clinical education. Worryingly, low awareness basic privacy and security measures appears common amongst medical students. In Ireland, little is known regarding smart-phone app ownership and use. This study sampled Irish undergraduate medical students at a single site. A 31-item questionnaire was developed by the primary researcher following a preliminary literature review and subsequently underwent peer review. The questionnaire was distributed by means of a paper survey. Non-probability convenience sampling was conducted at educational sessions at a single site to students of all years of a medical undergraduate curriculum as per ethics approval. Collected data was analysed using SPSS Statistics 20. The internal consistency of the questionnaire as measured by Cronbach’s alpha was high (α=0.951). The survey response rate was 34.8% (317/909) with 80.8% (256/317) of respondents owning a smart-phone. A greater percentage of preclinical students, 83.4% (151/181) owned smart-phones as compared to older students, of which 77.3% owned such a device (105/29). More clinical students (78.1%) used medical apps as compared to preclinical students (57%). The two most popular brands were Apple and Samsung devices. Of those who owned a smart-phone, 65.6% (168/ 256) reported using medically-related apps. Students used apps predominately to aid their study. While 69.9% (179/256) of respondents trusted the information provided by the medical apps they used, only 42.2% (108/256) verified whether app content was correct. In relation to other learning methods, 38.3% (98/256) said they would prefer to use an app instead of a textbook, 23% (59/256) as compared to a lecture, although 50.8% (130/256) would prefer an app to other online information. High rates of smart-phone ownership and medical app use exist amongst Irish medical students. While the majority of students trust the apps they use, only 42% verified whether the content of the apps they used was correct. Students require greater guidance when using apps as part of their learning. Universities should educate students regarding such use and provide them with recommendations and guidelines of app use as approved by faculty following a peer review process.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".