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Record W1976055810 · doi:10.1177/0309132508089826

Understanding the reproduction of health care: towards geographies in health care work

2008· article· en· W1976055810 on OpenAlexaff
Gavin J. Andrews, Josh Evans

Bibliographic record

VenueProgress in Human Geography · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHealth careHealth geographyWork (physics)Diversity (politics)ReproductionSociologyConsumption (sociology)Public relationsHealth policySocial scienceInternational healthEconomic growthPolitical scienceEcologyAnthropology

Abstract

fetched live from OpenAlex

There has been only a partial geographical engagement with the production of conventional health care. Whilst medical geography maps aggregate supply and demand features, the geography of health focuses more on consumption and social and cultural contexts. More specifically, apart from a handful of published studies, both of these fields have overlooked how health care is continually reproduced in places by workers. In response to these shortfalls in the literature, we call for attention to geographies in health care work. In support, we describe the geographies that characterize the new health care and, using therapeutics as an example, outline how clinical concepts might provide secure foundations for research. A final discussion outlines the multiple people, places and relationships that could be investigated. Developing geographies in health care work would provide sensitive insights into the complexity, diversity and daily operation of health care.

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)0.005
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.009
Science and technology studies0.0040.026
Scholarly communication0.0120.022
Open science0.0020.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.085
GPT teacher head0.350
Teacher spread0.265 · 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

Citations96
Published2008
Admission routes1
Has abstractyes

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