Strategies in transforming standard hospitals and clinics for COVID-19 treatment / Naveen Jayakumar Vijhay Keerrthi
Bibliographic record
Abstract
In Malaysia, total COVID-19 cases as of 7th June 2021, is 622,086 and total death of 3,460. Initially, Ministry of Health has assigned 11 government hospitals and UMMC (University Malaya Medical Centre) to treat COVID-19 patients. As of quarter 3 2021, almost all public hospitals and 96 private hospitals have agreed to provide COVID-19 treatment during this state of emergency. With surging numbers of COVID-19 cases more hospitals and even clinics are required to manage the patients. However, many of these hospitals and clinics hospitals lack of specific resources, flexibility and expertise to accommodate COVID-19 patients with confirmed symptoms. Therefore in this study, systematic study will be conducted to ascertain material and human resources, facilities upgrades and changes in operations required to manage COVID-19 patients in hospitals and clinics. Therefore, the aims of this study are to evaluate the best management practices (BMPs) worldwide in terms of infrastructure, logistics, and Standard Operating Procedures (SOPs) in COVID-19 treatement hospitals and to propose BMPs and strategies to transform the standard hospitals in our country to COVID-19 treatment hospitals for treatment. To meet the objectives, checklist provided by WHO, was simplified and distributed to frontliners and their feeback was analayzed. Based on the analysis, patient management recorded highest percentage of 98%. Hospitals in Malaysia have well established the SOPs for patient management. However, 82% of respondents had shown low agreement for statement of COVID-19 plan is available to potentially refer or outsource care of non-critical patients to alternative health facilities. By implementing the checklist in non-covid hospitals, it can be transformed to COVID-19 treatment hospitals immediately to support the increase number of cases. In addition,our community should follow all the SOPs in order to support the government, and healthcare providers, to curb the transmission of this virus, this is everyone’s responsibility.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".