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Enregistrement W2908662426 · doi:10.47339/ephj.2018.67

General radon gas knowledge test assessment for BCIT students

2018· article· en· W2908662426 sur OpenAlexvenueaboutno aff
Jamie Zhang, Environmental Health BCIT School of Health Sciences, Helen Heacock, Jeffrey Ma

Notice bibliographique

RevueBCIT Environmental Public Health Journal · 2018
Typearticle
Langueen
DomaineHealth Professions
ThématiqueRadioactivity and Radon Measurements
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRadonHealth hazardRadon gasTest (biology)Significant differenceEnvironmental healthGeographyHazardPsychologyMedicineGeologyMathematicsStatisticsPhysics

Résumé

récupéré en direct d'OpenAlex

Background Vancouver is located in a generally low-radon hazard zone. However, other parts of British Columbia such as the BC Interior or Northern BC are classified as high-radon hazard zone (or zone 1) due to the geological composition of rocks and soils in those areas. Despite the significant health risks associated with radon gas exposure, many BC residents and people across Canada have little to no knowledge regarding the topic. Since Post-secondary schools, such as the British Columbia Institute of Technology (BCIT), are places where knowledge is distributed and shared to our societies, it is important to assess students’ general knowledge background regarding radon gas. The result can then be extrapolated to the general populations. Methods An electronic survey was conducted to determine whether students in the six schools at BCIT have different background knowledge level regarding radon gas. The survey also determines students’ radon background knowledge based on different geographic regions they reside. The survey was conducted in-person at three main locations across BCIT’s Burnaby campus. It was administered using Google Forms and distributed to participants on Microsoft Surface 2. Results The One-way ANOVA statistical analysis result indicated that there is a significant difference in mean radon survey scores among the six various BCIT schools(p=0.009). In addition, the Tukey Test revealed that students from the School of Health Science have an average radon survey score which is significantly different when compared to students from the School of Business. However, it was found that there is no significant difference in the mean radon survey scores between the School of Business and other schools at BCIT. Nonetheless, it was evident that the School of Health Science students had relatively higher radon survey scores and thus, were more knowledgeable regarding radon gas compared to students from the other five schools. When analyzing survey scores among students residing in various geographic regions, the test showed that there is no significant difference in mean radon survey scores among BCIT students living in various geographic locations(p=0.46). Conclusion Based on the result of the study, the result showed that there is a significant difference in radon gas knowledge among BCIT students who majored in different schools. The School of Health Science students were more knowledgeable regarding the topic of radon gas compared to students in other schools. Nonetheless, all BCIT students achieved an average radon survey score of less than five out of ten, which was considered a failure score (Less than five out of ten). This showed that most BCIT students had very limited knowledge regarding radon gas and there were very limited amount of educational initiatives or campaigns available for students at BCIT. BCIT’s student association is recommended to create educational sessions across campus to raise student awareness regarding radon gas. At the community level, governments and various agencies such as the BC Lung Association need to work together to create radon awareness campaigns across BC and the rest of Canada. In order to get a more accurate representation of the radon gas knowledge level among people in BC, more research studies need to be conducted in other schools or general population groups.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,316
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0050,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,146
Tête enseignante GPT0,473
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission2
Résumé présentoui

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