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environment, but especially more so in the medical and health care environment where being complacent, negligence and out of date with work related advances could make the difference between life and death outcome in patients.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.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".