The impact of e-learning in workplace: focus on organizations and healthcare environments
Bibliographic record
Abstract
Although there has been much research on e-learning in the educational context, far less has been written about e-learning in the workplace. The purpose of this review is to draw together what research has been done on e-learning in the workplace to inform future researchers. E-learning is one answer to sweeping global changes, labor market and productivity issues. The review shows that e-learning is being spurred on in Canada by three drivers: the global economic context, the human capital context, and the information and communication technology context. The paper shows that the employers can integrate individual learning with organizational needs and provide employees with the knowledge and skills they need. Thus employee receives the modules of information and learning that fit their current need. Cost effectiveness was cited as one top reason to use e-learning, especially f or organization that are already using ICTs in their work processes. Researchers posited that ICTs are increasingly playing an important role in organizations and society's ability to produce access, adopt and apply information. In addition t ere is cost saving in terms of time. As in all types of working environment, but especially more so in the medical and health care environment where being complacent, negligence and out of date with work related advances could make the difference between life and death outcome in patients. There is a constant need to rapidly train and retrain the workforce in new technologies, pr ducts, and services found within the work place setting. Finally the paper looked into its benefits and barriers to e-learning.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".