Editorial: Advances in healthcare provider and patient training to improve the quality and safety of patient care
Bibliographic record
Abstract
This special issue of the Knowledge Management & E-Learning: An International Journal is dedicated to describing “Advances in Healthcare Provider and Patient Training to Improve the Quality and Safety of Patient Care.” Patient safety is an important and fundamental requirement of ensuring the quality of patient care. Training and education has been identified as a key to improving healthcare provider patient safety competencies especially when working with new technologies such as electronic health records and mobile health applications. Such technologies can be harnessed to improve patient safety; however, if not used properly they can negatively impact on patient safety. In this issue we focus on advances in training that can improve patient safety and the optimal use of new technologies in healthcare. For example, use of clinical simulations and online computer based training can be employed both to facilitate learning about new clinical discoveries as well as to integrate technology into day to day healthcare practices. In this issue we are publishing papers that describe advances in healthcare provider and patient training to improve patient safety as it relates to the use of educational technologies, health information technology and on-line health resources. In addition, in the special issue we describe new approaches to training and patient safety including, online communities, clinical simulations, on-the-job training, computer based training and health information systems that educate about and support safer patient care in real-time (i.e. when health professionals are providing care to patients). These educational and technological initiatives can be aimed at health professionals (i.e. students and those who are currently working in the field). The outcomes of this work are significant as they lead to safer care for patients and their family members. The issue has both theoretical and applied papers that describe advances in patient safety and training.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".