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Record W2022734753 · doi:10.3109/13561820.2013.807778

Interprofessional meetings in geriatric assessment units: a matter of care organization

2013· article· en· W2022734753 on OpenAlexafffundabout
Bernard‐Simon Leclerc, Nancy Presse, Aline Bolduc, Aurore Dutilleul, Yves Couturier, Marie‐Jeanne Kergoat

Bibliographic record

VenueJournal of Interprofessional Care · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeInstitut Universitaire de Gériatrie de MontréalUniversité de Montréal
FundersCanadian Institutes of Health ResearchUniversity of BristolMinistère de la Santé et des Services sociaux
KeywordsMedicineFamily medicineNursingPatient careGeriatric care

Abstract

fetched live from OpenAlex

Inpatient geriatric assessment units (GAUs) exist in Quebec, Canada, to deliver comprehensive, integrated care to older vulnerable patients. Most cases should be discussed at interprofessional meetings (IMs), but research has shown this not to be so for 39% of GAU patients. Consequently, a study was undertaken to (1) describe GAU team composition and (2) identify GAU and patient characteristics associated with case discussion at IMs at least once during a patient's stay. To this end, 877 hospitalization records from 44 GAUs were reviewed. Results showed most teams were composed of attending physicians, nurses, physical and occupational therapists, dietitians and social workers; 66% included clinical pharmacists and 43% liaison nurses. Multilevel modeling showed longer length of stay to be the strongest predictor of case discussion at an IM. Case discussion was also more likely for patients admitted via in- or inter-hospital transfer rather than via the emergency department, if the GAU included a liaison nurse, and if the GAU was not located in an urban area. In summary, case discussion at an IM depended more on how and where a patient was admitted than on the patient characteristics per se, suggesting that this is a matter of care organization.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0110.000

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 head0.010
GPT teacher head0.389
Teacher spread0.379 · 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.

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

Citations11
Published2013
Admission routes3
Has abstractyes

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