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Record W2129737009 · doi:10.18550/ijhpm.080115.0506

Battling for Bed Space in Kingston General Hospital, Ontario: 1927 to 1958

2015· article· en· W2129737009 on OpenAlexaffabout
Daniel Paluzzi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsQueen's University
Fundersnot available
KeywordsPoliticsHealth careGeneral hospitalInvestment (military)Space (punctuation)Sociocultural evolutionPublic hospitalPerspective (graphical)Public relationsSociologyMedicinePolitical scienceEconomic growthNursingFamily medicineEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

Urban hospitals can be gargantuan, heterogeneous complexes that result over decades of acquisition, assimilation, and reconstruction. Their layout effectively represents sociocultural and economic shifts in how healthcare is delivered to the community-at-large. This includes technological innovations that revolutionised the standard of care and spawned new medical subspecialties; however, certain services were prioritised over others when new space was created. To examine a hospital’s investment in specific departments, the novel method of counting beds was used. Bed tallies were compared to social and political events affected hospital structuration. Kingston General Hospital between 1927 and 1958 was chosen for its complete records and multiple expansions. The study period concludes before the introduction of provincial public healthcare to allow analysis from a uniform economic perspective. Hospital renovations were shown to either redistribute or expand services, with preference for the former.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.110
GPT teacher head0.448
Teacher spread0.338 · 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 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
Published2015
Admission routes2
Has abstractyes

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