IMPACT OF COVID-19 PANDEMIC ON WELLBEING OF DOCTORS IN KASHMIR
Bibliographic record
Abstract
After the report of first Coronavirus case from China in December 2020, neither the infection stopped nor its reports. The infection spread so fast that within less than a quarter of year, WHO declared it as a pandemic (March 2020). This pandemic known as Covid-19 pandemic affected almost every life on this planet and caused unprecedented injury to the global economy, job market, people’s livelihood, education, development and social life. The impact of this pandemic on the doctors and other healthcare practitioners was more severe as they were the frontlines and the main actors to reduce its impact. This pandemic has increased the workload and decreased the recovery opportunities of the doctors across the globe. It affected them both physically and psychologically. The impact has been more severe on the doctors of Kashmir as it is reeling under conflict from more than three decades now. The main aim of this paper, the data for which was gathered through a survey among the allopathic doctors of Kashmir, is to understand and document the impact of Covid-19 pandemic on the wellbeing of the doctors in Kashmir.
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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 | 0.022 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".