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Record W2068023601 · doi:10.5172/hesr.2007.16.2.195

Housing, Fuel Poverty and Health: A Pan-european Analysis

2007· article· en· W2068023601 on OpenAlexaboutno aff
Sharon Friel

Bibliographic record

VenueHealth Sociology Review · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyHuman rightsSocial determinants of healthCulture of povertyEconomic growthPolitical scienceSociologyBasic needsEconomicsLawHealth care

Abstract

fetched live from OpenAlex

HOUSING, FUEL POVERTY AND HEALTH: A PAN-EUROPEAN ANALYSIS Jonathan Healy Ashgate Publishing 2004, 249 pp, US$99.95 ISBN 0 7546 4218 6This is a very important book. Housing is a basic human right, embedded in the Universal Declaration of Human Rights (UN 1948), and in major international human rights treaties such as the International Covenant on Economic, Social and Cultural Rights (UN 1966). More recently, the Ottawa Charter blueprint for health promotion identified the prerequisites for health as being peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice and equity (WHO 1986). It is against this backdrop of international law and governance that the author tells the story of inequity in a basic necessity for health: adequate housing.The book embraces primarily housing-related, structural, and material determinants of health, but contextualises the observations within cultural and social policy considerations; describing how these upstream conditions shape (beneficially or otherwise), housing related matters and subsequently, health. Specifically, using a comparative framework, it sets out to examine, for Europe, the relationship between domestic energy efficiency, fuel poverty and related health impacts. The Republic of Ireland is used as a specific country context case study on the premise that it has high rates of fuel poverty, excess winter mortality, energy inefficient housing, high domestic energy consumption, and greater environmental emissions. While the introductory chapter rationalises the approach taken throughout the book, no explicit working definition is given for fuel poverty. Just as food poverty encompasses a number of domains including nutritional, social and health connotations, it would have been useful to make clear, upfront, the constituents of fuel poverty. Following from this, the book would benefit from a conceptual framework at the beginning, providing a heuristic device from which to consider the various ways fuel poverty and related socio-environmental factors affect health.The book makes excellent use of existing dataseis from a diversity of sources to illustrate systematic inequalities in key components of housing related socio-environmental determinants of health, both between countries and within countries. Chapters two to nine explore the empirical data, demonstrating marked gradients across Europe in housing conditions, levels of deprivation, affordability and general satisfaction with housing. The book highlights data limitations within the European countries, but is not unduly constrained by them. A key message from the book is the usefulness and necessity for harmonisation of public health related data and development of multilevel surveillance systems. In Chapter three, a new approach to measuring fuel poverty (and hence its definition) provides the mechanism by which to quantify and demonstrate the severity of fuel poverty within Europe. In an economically rich country such as Ireland, a remarkable 17% of households were found to be fuel poor.The final results chapter combines the various risk factors, explored until now in singular fashion, and assesses their impact on seasonal mortality, specifically excess winter mortality. It is hard to imagine that the inhabitants of Portugal - a country which provokes images of hot summers and unexceptional winters - experience the highest seasonal variation in mortality across the EU, with a winter increase of 28%. However, using a collection of datasets from 1988-97, Healy demonstrates that relative excess winter mortality is highest in southern Europe, Ireland and the UK. …

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.344
Teacher spread0.297 · 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 teacher head, not a consensus.

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

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

Citations19
Published2007
Admission routes1
Has abstractyes

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