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Record W1391653

Insights from the bar: A model of interaction

2012· article· en· W1391653 on OpenAlexvenueaboutno aff
Kerstin Huth, Sebastian Loth, Jan P. de Ruiter

Bibliographic record

VenueHealth reports · 2012
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceGermanPerspective (graphical)IntrospectionQuality (philosophy)Artificial intelligenceWorld Wide WebPsychologyGeographyCognitive psychology
DOInot available

Abstract

fetched live from OpenAlex

This study focuses on recent changes in hospital use by seniors (aged 65 or over) who have been treated as inpatients in general and psychiatric hospitals for less than one year with functional psychoses and clinical disorders. The data analyzed are hospital separations for the age groups 65 to 74, 75 to 84, and 85 and over, for the period 1980-89. The data are derived from hospital morbidity statistics submitted annually to Statistics Canada by the provincial ministries of health. Age-specific separation rates for the age groups 75 to 84, and 85 and over increased during 1980-89. However, for the age group 65-74, the rates were fairly stable and in some instances decreased. These increases in short-term hospitalization of the seniors 75 and over are evidence of the appreciation of their treatability and of the improved recognition of these disorders in this age group. Adverse reactions to prescription drugs resulting in psychiatric symptoms may also be a factor in the increasing hospitalization rates. The largest increases in age-specific rates and bed days were for the age group 85 and over. For this age group, the separation rates were higher for men than women. In this age group in 1986, a higher proportion of women than men lived in institutions. For seniors living outside institutions, a larger percentage of men than women lived alone. This situation likely promotes more social isolation for men than women, and may be an important factor in the higher hospitalization rates among men.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.067
GPT teacher head0.321
Teacher spread0.254 · 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 designOther design
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

Citations5
Published2012
Admission routes2
Has abstractyes

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