Attitudes and Behaviors of Health Care Professionals Towards Preventive Measures Against COVID-19
Bibliographic record
Abstract
The purpose of this study is that healthcare professionals play the most significant role in tackling pandemic COVID-19 and are considered as the most vulnerable and at-risk population for infection. An effective response to a pandemic depends on the attitudes and behaviors of physicians, nursing, staff, lab technicians, and other support staff. The study was conducted to explore the attitudes and behaviors of health care professionals towards preventive measures against COVID-19. The study was designed following the positivistic research paradigm hence cross-sectional survey research was selected as the most appropriate design. For the purpose of data collection, a self-administered structured questionnaire was developed and used. The survey was conducted during the month of March 2020 in Punjab through an online data collection method from 150 health care professionals working in various public sector hospitals in Punjab. The questionnaire was uploaded on the survey monkey website and shared on various social media platforms to collect data in order to get responses. Results show that self-reported anxiety level is high among physicians and nurses as compared to technical and support staff. Data shows that there are significant differences in attitudes and behaviors towards preventive measures against pandemic COVID-19 between physicians and nurses especially about the adoption of various techniques for improving immunity. It was also found that there are significant attitudinal and behavioral differences according to sex, region of residence, and marital status of health care professionals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".