Organization and management of maintenance indexed to risk factors in healthcare environment
Bibliographic record
Abstract
Currently, in a similar way to the policies adopted by the organizations, the hospitals lay an enormous effort in the search of the maximum efficiency of the organizational flows. This results in the improvement of the provided services and patient safety, taking into account the profitability of the physical resources and economic conditions. To achieve these goals, it is necessary not only to increase the efficiency of medical equipment throughout their life cycle, but also to prevent the risks associated with their handling, with emphasis on patient safety. The concepts of maintenance management and risk assessment have been evolving towards the adequacy of the policies most adapted to the nature of the facilities and equipment, whether by economic, functional or other classification. The concept of risk in a hospital environment is very diverse. There are physical, chemical, biological and psychosocial risks. Although all risks are important for the safety and well-being of patients, this dissertation/project focuses mainly on biological risks (infections due to viruses, fungi, bacteria and parasites), and physical risks with a special focus on electrical risk. These risks can be partially indexed to the organization and management of the maintenance of hospital facilities and equipment, as this can help to prevent risks; However, with a good evaluation and management of these, maintenance costs can be reduced and unexpected interventions can be avoided. In this context, this article analyzes the main electrical and biological risks, in order to establish a cause-effect relationship with the maintenance policies carried out by the institutions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".