Analysing the dynamics of the Indian pharmaceutical industry in light of the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic is a global crisis that caused economic disruptions. India faced challenges like limited resources and increasing demand for essentials, including medicines and supplies. To analyze this, time-series data was extracted to study the impact of the pandemic on India’s major retail pharmaceutical companies. Net sales and total revenue significantly increased post-pandemic and were especially noticeable in the third quarter of 2020. This resulted from a spike in covid cases and price hikes on major drugs needed to treat the disease. While e-commerce has thrived in other areas, rising demand and growth opportunities are leading it to make its way to retail pharmacies. Restriction of movement led to increased users of e-pharmacy. A survey using an electronic questionnaire was conducted on 190 participants across tier 1, 2, and 3 cities to understand consumer behaviour towards e pharmacy. Common age groups were tech-savvy youth aged between 15 and 30 years. 55% of respondents were e-pharmacy users before lockdown restrictions. Other data points including feasibility and delivery time were positive in tier 1 and 2 cities as opposed to tier 3 cities which can be attributed to logistics challenges. 78.3% of the study participants are likely to recommend and use the service post the pandemic. The pandemic has had a small impact on E-Pharmacy, but a shift at its core has begun, which has a promising future as observed through primary research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".