Issues of risk communication : gaps in knowledge and perception of human health risk due to climate change induced heat wave in Winnipeg
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".