Scalability and Sustainability Issues of Mobile Learning in Health Professions Education
Bibliographic record
Abstract
Mobile learning has been used increasingly in the past decades in different health profession education, and advancement in technology have produced different forms of mobile learning development modalities such as simulations, virtual patients, online courses and many interactive contents. However, many of those efforts’ outcomes failed to live up to their promises; hence, those were not widely adopted and became unsustainable. A literature review has been done through a literature search conducted across three databases – Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE (Ovid) and Google scholar – for studies describing or evaluating different mobile learning platform as used for education and professional development of different health professions based on user experience, perceived barriers, facilitating factors to scale it up and make it sustainable. This paper tends to identify factors or actions which are considered to optimize the experience and satisfaction of different stakeholders, help scaling up and identify strategies for sustainability of mobile learning interventions for health professions education. CBMJ 2025 January: Vol. 14 No. 01 P: 186-191
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 | 0.034 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".