The Impact of COVID-19 Outbreak on the Tourism Needs of Algerian Population
Bibliographic record
Abstract
This research aims to understand the vision and the reaction of the population towards tourism and holidays during this period of the COVID-19 pandemic. It investigates also the tourist needs of the Algerian population after the closure of international borders. Methods: The data were collected using mixed quantitative and qualitative methods through a questionnaire applied to 203 people in different regions of Algeria (a North African country) from 1st June to 13 July 2020. Results: The needs of Algerian tourists are characterized by a great need for leisure to relieve psychological stress caused by COVID-19 (M = 25.33) among the study sample (p <0.05). The results also show an average need to rationalize the costs of tourist services (M = 5.26) according to the respondents (p <0.01). This is in addition to the great need (M = 7.75) among respondents (p <0.05) of the awareness that the tourism sector can contribute to the economic recovery in Algeria after the confinement period. About 75.86% of respondents demand the cleanliness of tourist sites, while 69.95% recommend improving safety because of the size of tourist sites in the Algerian territory and also measures related to social distancing. The results show that 53.69% of respondents preferred the month of August to go on vacation, 29.06% chose the month of September, and 17.25% would prefer the months of October, November and December since they expect a reduction in the risks of the COVID-19 pandemic. Conclusions: The COVID-19 pandemic has affected the tourism needs of the Algerian population, which has become increasingly aware of the consequences of the pandemic in relation to their health and on the country's economy. These results can help the authorities of the tourism sector to better understand and identify the tourism needs of this population in the current period and after the COVID-19 pandemic.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".