Can a smartphone app improve medical trainees’ knowledge of antibiotics?
Bibliographic record
Abstract
OBJECTIVES: To determine whether a smartphone app, containing local bacterial resistance patterns (antibiogram) and treatment guidelines, improved knowledge of prescribing antimicrobials among medical trainees. METHODS: We conducted a prospective, controlled, pre-post study of medical trainees with access to a smartphone app (app group) containing our hospital's antibiogram and treatment guidelines compared to those without access (control group). Participants completed a survey which included a knowledge assessment test (score range, 0 [lowest possible score] to 12 [highest possible score]) at the start of the study and four weeks later. The primary outcome was change in mean knowledge assessment test scores between week 0 and week 4. Change in knowledge assessment test scores in the app group were compared to the difference in scores in the control group using multivariable linear regression. RESULTS: Sixty-two residents and senior medical students participated in the study. In a multivariable analysis controlling for sex and prior knowledge, app use was associated with a 1.1 point (95% CI: 0.10, 2.1) [β = 1.08, t(1) = 2.08, p = 0.04] higher change in knowledge score compared to the change in knowledge scores in the control group. Among those in the app group, 88% found it easy to navigate, 85% found it useful, and about one- quarter used it daily. CONCLUSIONS: An antibiogram and treatment algorithm app increased knowledge of prescribing antimicrobials in the context of local antibiotic resistance patterns. These findings reinforce the notion that smartphone apps can be a useful and innovative means of delivering medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".