Which practice is best to manage the Hidden curriculum for the best use of mobile devices in clinical practice? A systematic review.REVIEW
Bibliographic record
Abstract
Objective: To evaluate the literature regarding the practices to manage the hidden curriculum for the best use of mobile devices in clinical practice. Study Design: Systematic Review. Setting: Articles selected for review from Canada, United Kingdom, Japan, Ireland and Saudi Arabia. Period: July to Dec 2023. Methods: Following databases were searched: PubMed (12,579), the Cochrane Library (348), scopus (84), PsycInfo (21), CINAHL (220), Google Scholar (1,414). Primary variable (Evaluation of the development of clinical skills made possible by mobile devices) and secondary variable (to determine how satisfied students are with their mobile learning experience). The quality of study was critically appraised according the Critical Appraisal Skills Programme (CASP) scale. Results: The research findings indicate that using mobile devices into medical education has a variety of effects. Positive instructor perspectives, more student involvement, and higher learning outcomes were frequently reported by participants. Medical students' growth of technological competency and readiness for the changing healthcare landscape have been found to be accelerated by mobile devices. The integration of virtual simulations and applications that are interactive has had a positive impact on the development of clinical abilities. Positive effects included themes of individualization, collaborative learning communities, and a better understanding of patient-centered care. On the other hand, issues including the digital divide, diversions, and security threats were recognized as obstacles that called for a careful strategy to reduce any negative effects. When everything is considered, the findings confirm the revolutionary potential of mobile device incorporation in medical education and highlight how it helps to create a dynamic, technologically advanced learning environment for prospective medical professionals. Conclusion: This study provides insight on how adding mobile devices into medical education has a revolutionary effect. The research indicates enhanced learning outcomes, increased student involvement, and altering faculty perspectives through insightful stories and compelling arguments.
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 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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".