Bibliographic record
Abstract
The introduction and use of information and communication technologies (ICT) in health care, particularly the electronic health record (EHR), may be seriously hampered or delayed by the lack of available human resources with the necessary skills and competencies in e-health. A number of different types of professionals are needed, and an appropriate mix of skills and workers who can complement one another in the final deployment of the EHR and in the appropriate and best use and management of the health information it contains. These include health informatics (HI) professionals or health informaticians, health information management (HIM) professionals, and others, with not only knowledge of ICT, but also knowledge of the health system, data standards, and interoperability across platforms; privacy and security of health records; human factors and process engineering; project management and technology adoption; and user-supporting mechanisms. A human resources strategy is needed to address the current shortage of skilled workers and to develop a long term strategy for education and training of e-health personnel necessary to ensure the continued quality of health data collected, its security and confidentiality, and to manage and maintain the systems and data in the future.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".