Point-of-Care diagnostic of SARS-CoV-2: knowledge, attitudes, and perceptions (KAP) of medical workforce in Italy.
Bibliographic record
Abstract
INTRODUCTION: The present study aims to characterize knowledge, attitudes and beliefs in a sample of medical professionals towards point-of-care (POC) rapid tests for SARS-CoV-2 in Italy (April 2020). MATERIAL AND METHODS: A total of 561 professionals (42.6% males, 26.9% ≥ 50-year-old) compiled a specifically designed web questionnaire on characteristics of POC rapid tests. They were asked whether they would change daily practice and make clinical decisions according to POC tests. Multivariate odds ratios (aOR) for predictors of propensity towards the aforementioned behavioral outcomes were calculated through regression analysis. RESULTS: Overall, only 51.9% knew the official recommendations of the Italian Health Authorities for POC tests, while 26.0% of respondents considered POC tests for COVID-19 highly reliable. Still, 40.3% of respondents would change daily practice because of such tests, and 38.5% would make clinical decisions based of their results. Actual understanding of specificity and sensitivity of POC tests was not associated with assessed behavioral outcomes: main positive effectors were identified in perceived reliability and usefulness of rapid tests, acknowledging the existence of official recommendations, understanding the limited clinical implications of POC tests, and working as occupational physicians were characterized as negative effectors. Conclusions. Propensity of sampled professionals towards POC tests for COVID-19 was diffusely unsatisfying. While actual understanding of accuracy of such tests was not a main effector of propensity, previous experiences with other POC tests in daily practice, particularly among occupational physicians may have impaired overall acceptance of such instruments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".