The COVID-19 impact: Insights into the pharmaceutical sector through 10-K reports
Bibliographic record
Abstract
COVID-19 has lasting impacts on pharmaceutical companies, and it is crucial for managers to fully understand and monitor the impact areas to prepare for future organizational resilience building and transformation. However, the research on COVID-19 impacts is still discrete and anecdotal. Based on a textual analysis of 447 public pharmaceutical companies’ 10-K annual reports and statistical analysis of their financial data, this research systematically identifies major impact areas experienced by public pharmaceutical companies during COVID-19, the patterns of impacts (distribution and sentiment of impacts), and their association with firm characteristics. A topic modeling analysis reveals four impact areas, including new product development processes, sales and operations, COVID-19 treatment and prevention drug early development, and COVID-19 drug clinical trials. Companies reporting COVID-19 drug clinical trials are the most optimistic in the sample, while companies reporting sales and operations are the most pessimistic. Furthermore, firms exhibit heterogeneity in terms of the impacts they experience. Those whose primary business focus is on research and development are more likely to report impacts related to new product development processes, while those with diverse business focus tend to highlight issues on sales and operations. The impact areas uncovered in this research point out the domain for managers to watch for potential transformation, whereas the findings on firm heterogeneity guide managers to make efforts tailored to their firm characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".