Comparison of restaurant inspection report results and its corresponding star ratings on Yelp and Google Reviews
Notice bibliographique
Résumé
Background: In the current culture of dining-out, there is a greater emphasis on the overall dining experience at restaurants and less of a concern regarding food safety. The public often relies on consumer-generated review websites, such as Yelp and Google Reviews, to decide on where to eat. Each restaurant is often rated out of 5-stars based on factors such as customer service and food quality. The public perceives a restaurant with a 1-star rating poorly, whereas a restaurant with a 5-star rating is seen as excellent. Moreover, the aspect of food safety is determined by Environmental Health Officers (EHOs) who conduct inspections and assign hazard ratings to restaurants, which describe them as a low, moderate, or high-risk food premises. These inspection report results can be disseminated to the public online or through a placard system by the health authority. Currently, in most cities, there is no linkage or display of inspection report results on consumer-generated review websites. Methods: Secondary data was collected from publicly available online sources: Fraser Health’s restaurant inspection reports and two consumer-generated restaurant review websites – Yelp and Google Reviews. The author analyzed 170 randomly selected restaurants from the three most populous cities under Fraser Health’s jurisdiction (British Columbia, Canada): Surrey, Burnaby, and Abbotsford. Only independent restaurants and their routine inspection reports were considered in this study. The following data was obtained from each of the restaurant’s available routine inspection reports: current hazard rating, the average hazard score, and total number of critical violations (CVs). These variables were then compared to the current star rating found on Yelp and Google Reviews. Results: A total of six statistical analyses were conducted: two chi-square tests and four correlational analyses. When comparing the current hazard rating of the restaurant and their current star rating using chi-square tests, p = 0.0855 for Yelp and p = 0.0739 for Google Reviews. Furthermore, in all four correlational analyses, a negative linear relationship was observed, but only three resulted in statistically significant results. When comparing the average hazard score of the restaurant’s routine inspections and their current star rating, p = 0.0591 for Yelp (power = 47.21%) and p = 0.0000 for Google Reviews (power = 99.97%). When comparing the restaurant’s total CVs from routine inspections and their current star rating, p = 0.0001 for Yelp (power = 97.29%) and p = 0.0000 for Google Reviews (power = 100%). Conclusions: The findings of this study demonstrated that prescribed food safety evaluations largely align with the customer perception of restaurants. Although three out of six statistical tests resulted in statistically significant results, overall, it appears that restaurants with a higher star rating have lower number of CVs and lower average hazard scores. Even though this ideal relationship was established, the importance of safe food handling practices and serving safe food to the public should not be overlooked. Consumer-generated restaurant review websites are an excellent avenue to promote food safety within the overall culture of dining-out at restaurants.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,010 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».