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Record W807029506

Overweight And Obesity In Canada: Understanding The Individual and Socio-environmental Determinants

2009· dissertation· en· W807029506 on OpenAlexfundaboutno aff
Theodora Pouliou

Bibliographic record

VenueMacSphere (McMaster University) · 2009
Typedissertation
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersAmerican Association of GeographersHeart and Stroke Foundation of Canada
KeywordsOverweightObesityEnvironmental healthGeographyPolitical scienceMedicineEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

<p> This research examined the geographic variability as well as the individual-and neighbourhood-level determinants of overweight and obesity in Canada. Overweight and obesity represent a significant public health problem with grave implications for individuals as well as populations. Over the past two decades, the prevalence of overweight and obesity has reached epidemic proportions with the most substantial increases observed in economically developed countries. The World Health Organization indicated that globally 1.6 billion adults (age 15+) are overweight and at least 400 million adults were obese. In a Canadian context, recent data from Statistics Canada confirms that over the past twenty-five years, adult obesity rates in Canada have doubled (23% ), while childhood obesity rates have nearly tripled. </p>
\n<p> Until recently, research has focused on biological and behavioural determinants of obesity, and currently there is a great deal of knowledge regarding the relationships between weight status and various risk factors at the individual-level (e.g. age, sex, socioeconomic deprivation, diet, physical activity). However, the majority of existing research has ignored the potential role played by the environment in the development of these conditions, despite a growing consensus that environmental and/or societal constraints may be major influences on increasing prevalence rates. </p>
\n<p> Using data from the Canadian Community Health Surveys and the Desktop Mapping Information Technologies Incorporated spatial database, this research addressed the following objectives: (I) to examine sex-specific spatial patterns of overweight/obesity in Canada as well as investigate the presence of spatial clusters (2) to investigate the prevalence and determinants of overweight and obesity in Canada using spatial analysis and geographical information systems (GIS) and (3) to identify heterogeneities associated with the relationships between individual and socioenvironmental determinants and overweight and obesity at the individual-and community-levels. </p>
\n<p> Results revealed marked geographical variation in overweight/obesity prevalence with higher values in the Northern and Atlantic health-regions and lower values in the Southern and Western health-regions of Canada. Significant positive spatial autocorrelation was found for both males and females, with significant clusters of high values or 'hot spots' of obesity in the Atlantic and Northern health-regions of Alberta, Saskatchewan, Manitoba and Ontario. Results also demonstrate the important role of the built-environment after adjustment demographic, socio-economic and behavioural characteristics. With regard to the built environment measures, landuse mix and residential density were found to be significantly associated with BMI. This study also demonstrated significant differences at the area-level of analysis, supporting related research that has suggested that individual-level factors alone cannot explain variation in obesity rates across space. In particular, average dwelling value was related to BMI independently of individual-level characteristics. Ultimately, this research has demonstrated that Canadian urban environments play a small but significant role in shaping the distribution of BMI. Yet, reversing current trends will require a multifaceted public health approach where interventions are developed from the individual-to the neighbourhood-level, specifically focusing on altering obesogenic environments. </p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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.

Study designObservational
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
Published2009
Admission routes2
Has abstractyes

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