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Record W2121464975 · doi:10.1111/cag.12085

“It's not only for sick people”: The place of the local hospital in a rural northern Ontario community

2014· article· en· W2121464975 on OpenAlexafffundvenueabout
Elaine Wiersma, Rhonda Koster

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsLakehead University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)Community hospitalIdentity (music)EthnographyQualitative researchLocal communityWork (physics)NursingCommunity developmentState (computer science)SociologyMedicineGeographyEconomic growthPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Abstract The research explored in this paper is part of a larger project examining the experiences of aging‐in‐place in a rural, northern Ontario context where communities are in a state of economic instability or transition. We focused on a case study of one community in northwestern Ontario, and explored the role and social place of the hospital in a small rural northern town using focused ethnography. In total, 84 people were interviewed, including older adults, health service providers, and other community members. The local hospital played a significant role in the community. The hospital was viewed not only as a community resource and an institution that served the needs of the community, but participants described the hospital in ways that reflected the place of the local hospital in the community. Three main themes were evident emerging from this work: the hospital as a source of community uniqueness, the hospital as a source of security, and the hospital as a source of community development. The findings highlight the importance of a local community hospital, both in terms of added value and community identity.

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 categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
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
Bibliometrics0.0010.003
Science and technology studies0.0040.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2014
Admission routes4
Has abstractyes

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