Impacts of COVID-19 on tourism and management response from Banff National Park, Canada
Bibliographic record
Abstract
The COVID-19 pandemic posed challenges to the tourism sector globally. We investigated changes in visitor demographics, satisfaction level, and its determinants pre- and peri-COVID-19. Data were collected using questionnaire surveys in 2019 and 2021 within Banff National Park (BNP). The data analyses were based on a sample size of 1183 respondents by conducting factor analysis, correlation analysis and stepwise regression analysis. Results highlight that there were fewer international visitors and more local and domestic visitors during the pandemic. Park attributes were evaluated at a higher satisfaction level peri-COVID-19. The quality of the Park facilities and services were the most important satisfaction determinants pre- and peri-COVID-19, and all the Park COVID-19 measures and actions received positive experience from visitors. This research fills this knowledge gap by developing a better understanding in the change of visitor demographics and satisfaction level in BNP under the context of the pandemic. It also provides implication for both scholars and practitioners to understand the impacts of the pandemic on Park visitation. The study can provide insights for utilizing the pandemic as a transformative strength and for mitigating its negative impact on tourism industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".