Issues of risk communication : gaps in knowledge and perception of human health risk due to climate change induced heat wave in Winnipeg
Notice bibliographique
Résumé
There is a general consensus in the experts' community now that the global mean temperature has increased remarkably in recent decades, and anthropogenic activities have contributed significantly to such changes.It has been projected that such change in temperature will cause frequent and more intense floods, droughts, tornadoes and heat waves globally as well as in Canada in the forthcoming decades.What is the l) nature and level of knowledge about these emerginghazards and associated risks and 2) how these phenomena are being perceived both by the expert community and local residents were the key areas of inquiry of the present research.As heat waves carry a significant amount of risk through posing both the threat of heat-related illnesses and the aggravation of pre-existing health problems, the climate change-induced incremental change in the trends of heat wave frequency and intensity in the Prairie urban communities has become a major research and policy concem.ln consideration of the above, the specific study objectives were to: i) examine the state of knowledge and perception of climate change-induced heat wave hazards among the expert community; 1i) analyze the state of knowledge, perception and awareness of climate change in general and its associated heat wave hazards among the community members at the local level; iii) identify the gap that exists between 'scientific/technical' and 'local community' knowledge regarding heat waves; and iv) explore the effective risk communication strategies that could help increase the community's coping capacity.For the purpose of the present study as well as in consideration of the specific objectives of the present research, a model (i.e."Knowledge Model") of the concemed phenomena was formulated.This method combines both qualitative and quantitative iii approaches through the implementation of three distinct steps: i) the development of an Expert "Knowledge Model", ii) carrying out the face-to-face interviews, and iii) conducting a confirmatory questionnaire survey at the local community level in the City of Winnipeg.Analysis of the study have explored into the i) causes of heat waves, ii) risks associated with heat waves, iii) various effects of heat waves, and iv) potential preventive and mitigation options.A comparison between experts' and lay knowledge model has revealed that there are significant gaps which include the understanding of the complex earth and atmospheric systems, the relationships between variables relevant to climate change systems, misconception about the rise of global atmospheric mean temperature, conceptualizing cumulative effects of heat \ /aves, heat wave "risk estimation" by the residents for the City of Winnipeg and its communities while recent hydro- meteorological data confirmed that Winnipeg is one of the most susceptible cities to heat wavehazatds in Canada, and the role of precautionary measures in reducing mortality.Among the selected demographic and socio-economic explanatory variables, age, income status, level of education and gender were found to be modest predictors.Knowledge and perception of heat wave risks are thus also influenced by social and personal values, belief systems and previous experience.The findings have further revealed that not only risk messages need to address knowledge-gap areas, with clear and explicit statements with all of the intended points, serious efforts should be made to engage community level organizations and motivate people.In addition, effective risk messages need to be designed in ways: i) that communicate clearly and interestingly to the residents as well as to experts; and ii) that link to the issues and problems of daily life.Dr. C' Emdad Haque.Through your academic guidance and direction I was able to finish an interesting research topic that was very intellectually rewarding.Thank you for always having your door open and for taking the time to answer my numerous questions.I learned a great deal from you.I would also like to acknowledge the members of my committee for their guidance.Dr. Dave Hutton, thank you for your input, helpful direction
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».