Hot weather in a cold land : hot weather planning and vulnerable populations in Toronto, Canada.
Bibliographic record
Abstract
Background Climate change has resulted in changes to weather patterns and to the intensity of extreme atmospheric phenomena. One of the most significant of these changes is manifested as increased intensity and frequency of hot weather events. To mitigate the effects of extreme heat, many cities have implemented ‘hot weather’, or ‘heat wave’ response programmes. However, the effectiveness of these, and how well they protect vulnerable populations remain underresearched issues. Objective This thesis examined whether the people of Toronto who are most vulnerable to extreme heat, have been properly identified and served by Toronto’s Hot Weather Response Programme. Design This thesis utilised qualitative techniques in the form of a thematic critique of documentary sources and semi-structured interviews. Documents pertaining to Toronto’s Hot Weather Response Programme were analysed to gain an understanding of the evolution of the programme. Interviews were also conducted with vulnerable residents to understand their perceptions and experiences with hot weather and Toronto’s programme. Findings The thematic critique determined that over the time period 1999 to 2019, there were multiple changes to the criteria used for calling alerts/warnings, repeated revisions to the definition of vulnerability, shifts in communication techniques and drastic reductions in budget and services. The interviews indicated that recipients had limited knowledge of the negative consequences of hot weather, lived in conditions that were detrimental to their health, did not have mitigation plans, and the amenities that were available to them were insufficient to be protective. Discussion The findings indicate that the vulnerable populations of Toronto have been inadequately identified by health officials and the methods used to reach them have been poorly selected. These populations demonstrated a lack of knowledge of Toronto’s hot weather programme and of protective measures necessary during oppressive heat. Toronto’s once ‘cutting edge’ hot weather response program has been greatly diminished due to a lack of proper evaluations and budgetary cuts which underestimate the value of elements of the programme. Conclusions and recommendations This thesis demonstrated the benefits of proper planning and evaluation for public health programmes. It also illustrated how political or economic pressure can alter the delivery of services and can attenuate the potency of mitigation activities, such that they can instead increase vulnerability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".