MétaCan
Menu
Back to cohort
Record W4416422833 · doi:10.4324/9781032677552

Understanding Health, Illness and Society

2025· book· en· W4416422833 on OpenAlexaboutno aff
Alexis Paton

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicHistorical and modern epidemiology studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthHealth promotionIdeal (ethics)Health careHealth policySocial determinants of healthPoliticsSociology of health and illness

Abstract

fetched live from OpenAlex

This accessible book shows how social structures and social norms shape both individual and collective health. It provides readers with an insightful understanding of the relationship between society, health and illness and highlights issues to inform a progressive, patient-centred approach to contemporary healthcare. The book begins by discussing how health has been defined and understood over the last century before examining how social issues such as deprivation, class, employment, housing, gender, ethnicity and policy shape health and contribute to health inequities. The book then discusses public health initiatives such as health promotion and screening programmes, the impact of resource allocation and the role that politics and policy play in supporting a healthy society. To bring concepts to life, the book uses case studies from the United Kingdom, the United States and Canada. Guided by the fundamental principle that everything in a society has some effect or impact upon its health, and assuming no prior knowledge of the social sciences, this is the ideal book for healthcare students across nursing, medicine, midwifery and pharmacy, as well as anyone interested in the relationship between health and society.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.280
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.224
GPT teacher head0.375
Teacher spread0.151 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicHistorical and modern epidemiology studiesFrench-language works237,207