Baseline knowledge and attitudes on COVID-19 among hotels’ staff: A cross-sectional study in Kigali, Rwanda
Bibliographic record
Abstract
BACKGROUND: The World Health Organization declared coronavirus disease 2019 (COVID-19) as a global pandemic on the 11th of March, 2020. Hotels and other public establishments have been associated with higher transmission rates. Sensitisation of staff and strengthening of Infection Prevention and Control (IPC) practices in such settings are important interventions. This study assessed the baseline knowledge and attitudes on COVID-19 among hotels' representatives in Kigali, Rwanda. METHODS: A cross-sectional study was conducted among hotels' staff in Kigali in July 2020. A structured questionnaire was self-administered to 104 participants. Baseline knowledge and attitudes were assessed using a number of pre-test questions and mean scores were used to dichotomise the participants' responses as satisfactory or unsatisfactory. RESULTS: All of the 104 hotels' staff completed the self-administered questionnaires. Sixty-seven percent (n = 70) were male and 58% (n = 60) were aged between 30 and 44 years. The satisfactory rate of correct answers was 63%±2.4 (n = 66) on knowledge and 68%±1.7 (n = 71) on attitudes evaluation. Participants with University education were more likely to have satisfactory knowledge (AOR: 2.6, 95% C.I: 1.07-6.58) than those with secondary education or less. The staff working in the front-office (AOR: 0.05; 95% CI 0.01-0.54) and housekeeping (AOR: 0.09; 95% C.I: 0.01-0.87) were less likely to have satisfactory attitudes than those working in the administration. CONCLUSIONS: Hotels' staff based in the capital of Rwanda have shown satisfactory knowledge and attitudes regarding appropriate IPC practices for preventing the COVID-19 transmission. Educational interventions are needed to improve their knowledge and attitudes for better prevention in this setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".