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Record W4214701096 · doi:10.1177/00027642221075263

Dementia Care for Europeans in Thailand: A Geography of Futures

2022· article· en· W4214701096 on OpenAlexafffund
Geraldine Pratt, Caleb Johnston

Bibliographic record

VenueAmerican Behavioral Scientist · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaEconomic and Social Research CouncilNewcastle University
KeywordsDementiaFutures contractLeverage (statistics)Health careCare workPrivilege (computing)Economic growthIndigenousBusinessSocioeconomicsNursingPsychologySociologyMedicinePolitical scienceWork (physics)EconomicsDiseaseFinance

Abstract

fetched live from OpenAlex

We explore the creation of private care facilities around Chiang Mai in northern Thailand to provide dementia care for people from the Global North. We draw on three periods of ethnographic observation at care facilities, and interviews with Swiss and British owners and family members, as well as Thai managers and care workers. We locate this offshoring of dementia care from the Global North to South within existing underfunding of dementia care in the Global North and a "regime of anticipation" built around expected substantial growth in the numbers of people living with dementia. These facilities are opening new futures for those who migrate for care as they leverage their relative wealth and privilege to purchase care in Thailand. In line with other readings of international health migration, we note the negative impact of this state-supported privatized industry on the availability of nurses and care aids in public hospitals in Thailand. We then venture into less examined and expected futurities, namely, the opportunities these facilities provide to two groups of stigmatized Thai workers: transgender and Indigenous Karen caregivers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.321
Teacher spread0.307 · 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
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

Citations7
Published2022
Admission routes2
Has abstractyes

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