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Record W7161962413 · doi:10.82308/20554

Public health adaptation to climate change in the federalist states of Canada and Germany

2017· dissertation· en· W7161962413 on OpenAlexaboutno aff
Stephanie Austin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthClimate changeGovernment (linguistics)Adaptation (eye)Public policyFederalistLocal government

Abstract

fetched live from OpenAlex

Climate change is projected to have considerable impacts on human health, and public health institutions at all levels of government will need to adapt to protect the health of populations. The impacts of climate change are highly localized, thus federal systems are expected to have the inherent advantage of allowing for regional diversity and policy experimentation in adaptation, but are also prone to conflict and stalemates. In addition, local public health authorities often lack the capacity to adapt to climate change, despite being on the 'front lines' of climate impacts. Assistance is needed from upper-level governments for local-level public health adaptation to climate change. It is unclear, however, how different levels of government are interacting for public health adaptation or what form that support should take beyond vague calls to build capacity. This thesis examines the relationship between different levels of government in Canada and Germany for public health adaptation, framed in federalism, intergovernmental relations, and adaptive capacity concepts. This thesis aims to i) characterize how intergovernmental dynamics are patterned across national, regional, and local levels of government in federal systems for public health adaptation to climate change; and ii) examine how federal and regional governments could contribute to enabling and supporting local public health authorities' adaptive capacity in federal systems. This research is based on 28 semi-structured interviews in comparative nested case studies of Quebec, Canada and Baden-Württemberg, Germany. I find that coordination between levels of government specifically for climate change and health is rare, but climate change issues are occasionally discussed through existing methods of public health coordination. Interactions for public health adaptation between the federal and regional governments follow expected patterns of intergovernmental relations (i.e., loosely coupled in Canada, tightly coupled in Germany), but I find the inverse occurring in the relationship between regional governments and local public health authorities in each country. Additionally, adaptive capacity varies widely between local public health authorities, but all report having insufficient funding, and consequently staff, for adaptation activities. Based on interviewees' perspectives and needs, and complemented by adaptation literature, I identify specific measures upper-level governments can take to build local public health authorities' capacity for adaptation, grouped under the action-oriented and interrelated themes of: building financial capacity; fostering knowledge, knowledge translation and skills; collaborating and coordinating for shared knowledge; and claiming leadership. These findings contribute to adaptation literature by demonstrating how adaptation is occurring across multiple levels of government in the public health sector and characterizing approaches to in coordination and interactions in the two case studies. Moreover, this research supports decision-makers seeking to enable sub-national public health adaptation to climate change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.865

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0170.007
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.176
GPT teacher head0.472
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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