Establishing and streamlining the communication and coordination through emergency control room in a tertiary care hospital by upgrading the available resources in the pandemic of COVID-19
Bibliographic record
Abstract
As the COVID-19 pandemic started, a tertiary care hospital of central Gujarat faced problems in developing communication and coordination between the doctors as there was a lack of emergency control room (ECR) set up. This article documents the process of establishing ECR using locally available resources. The department of Community Medicine (CM) was made in-charge of ECR. All information technology (IT) equipment were supplied by hospital administration as per the demand. Various challenges experienced by the staff of CM were discussed with the authorities and specialists from respective departments and quick intermediate solutions were adopted. In July-2020, ECR was established with IT support which accelerated the sharing of laboratory findings and patient details to the treating physician. Dedicated smart phones were assigned to each floor providing real time patient updates through Whatsapp groups and video calling feature was used to establish effective communication between patients and relatives. The hospital also collaborated with a local NGO that provided manpower to address queries at ECR. This study concludes that focusing on utilising local resources and manpower and training the available personnel to handle the problems in the best possible ways made the system work properly at the time of crisis.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".