Rapidly established telehealth care for blood cancer patients in Nepal during the COVID-19 pandemic using the free app Viber
Bibliographic record
Abstract
National lockdown to control the spread of COVID-19 in Nepal started in March 2020. This lockdown mandated closure of private and public transportation. The patients with hematological malignancies were at risk of delayed consultation, admission and missing scheduled chemotherapy. Since there is no official tele-health or e-health system established in hospitals, we decided to use Viber, a free text and call app to trace and provide information about patient admission and treatment schedule. This use of Viber during the pandemic was found to be very helpful, none of the patients missed chemotherapy and we were able to admit more patients than before. Patients found this strategy very convenient and cost-effective and suggested that we continue this service in future even after the lockdown is lifted. This preliminary experience of using Viber for cancer care consultations in Nepal at the time of the COVID-19 pandemic suggests the utility and acceptability of using mobile technology to improve access to health care services in a low-income country. Further pre-planned well conducted studies are needed to assess the outcomes of using this technology.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".