Biomedical Engineering Profession – An Overview and Global Comparison of Staffing Criteria and Workforce
Bibliographic record
Abstract
Introduction: Biomedical Engineering is a specialized profession that incorporates engineering, science, technology, and medicine competence and responsibilities. A biomedical engineer who works at hospital and manages the biomedical engineering department by integrating all the health care technologies for patient safety is called a clinical engineer.Methodology: Rapid literature on the workforce as well as staffing criterion has been done by searching in PubMed, google scholar and relevant websites.Results: Variations in the staffing criteria exist across all the world. There are multiple staffing criterions put forward by various health system agencies. However, a standardised staffing criterion is lacking in many countries. The conventional staffing pattern developed primarily on the basis of number of patients needs to be modified by incorporating multiple components such as the nature of care delivery, number of biomedical devices used, average number of maintenance work orders received etc. The procurement and maintenance of biomedical devices are often get disrupted in developing countries due to inadequate staffing. Low-income countries depend on donations for procurement of medical devices, however most of these devices will stop working within a period of 5 years due to lack of maintenance.Conclusion: Developing an appropriate job description specific to the county and adopting a standardised staffing pattern could contribute immensely to the medical workforce as well as improving the quality of medical care.Manuscript HighlightsThis paper provides an overview of considerations used to develop staffing criteria for biomedical engineers across the globe. The paper also does a multicounty comparison on biomedical workforce published by the World Health Organisation in order to determine the various factors to be considered for developing a staffing criterion. High income countries like Australia, Canada and USA follows standards according to their requirements whereas the LMICs doesn’t follows a framed criterion. The paper discusses various models available in place which are used by various health administrative agencies to consider developing and regulating the staffing standards of biomedical engineers in their respective regions. The paper examines the health regulations considering the biomedical engineer staffing criterions developed by regulatory and administrative agencies from different countries. Based on all these analyses, recommendations are made on criterions to be considered for developing staffing pattern and implementing regulatory body for biomedical engineering profession.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".