Technology and Human Resources Management in Health Care
Bibliographic record
Abstract
Health care has lagged behind most industries and businesses in its adoption of information and communication technologies (ICT). Many of the current information technologies and those to be deployed and developed over the next few years (e.g. electronic health records, telehealth applications, elearning technologies, social networking via Web 2.0) could be of benefit in health care delivery and improvement of the quality, efficiency and effectiveness of health care services. The uses of technology in human resources management (HRM) can help improve the medical care that health professionals provide to their patients. For instance, technology can be used to maximize communication, collaboration and support between health professionals separated by distance, as well as provide immediate and up-to-date patient care information. ICT can also be used for distance training and education for those facing geographic isolation and provide a medium through which continued education can be maintained for both rural and urban health professionals. However, due to the differences in barriers to ICT use found for each group, such as computer illiteracy, geographic isolation or poor infrastructure, different steps need to be taken in order to ensure the successful implementation and use of information technologies in both urban and rural communities in developed and developing regions across the world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".