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Record W2059941393 · doi:10.1177/0269216311408992

Access to palliative care services in hospital: a matter of being in the right hospital. Hospital charts study in a Canadian city

2011· article· en· W2059941393 on OpenAlexafffundabout
Joachim Cohen, Donna M. Wilson, Amy Thurston, Rod MacLeod, Luc Deliëns

Bibliographic record

VenuePalliative Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Alberta
FundersVlaamse regeringFonds Wetenschappelijk OnderzoekUniversity of Alberta
KeywordsMedicinePalliative careHospital careNursingFamily medicineMedical emergencyHealth care

Abstract

fetched live from OpenAlex

Access to palliative care (PC) is a major need worldwide. Using hospital charts of all patients who died over one year (April 2008-March 2009) in two mid-sized hospitals of a large Canadian city, similar in size and function and operated by the same administrative group, this study examined which patients who could benefit from PC services actually received these services and which ones did not, and compared their care characteristics. A significantly lower proportion (29%) of patients dying in hospital 2 (without a PC unit and reliant on a visiting PC team) was referred to PC services as compared to in hospital 1 (with a PC unit; 68%). This lower referral likelihood was found for all patient groups, even among cancer patients, and remained after controlling for patient mix. Referral was strongly associated with having cancer and younger age. Referral to PC thus seems to depend, at least in part, on the coincidence of being admitted to the right hospital. This finding suggests that establishing PC units or a team of committed PC providers in every hospital could increase referral rates and equity of access to PC services. The relatively lower access for older and non-cancer patients and technology use in hospital PC services require further attention.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.385
Teacher spread0.312 · 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

Citations21
Published2011
Admission routes3
Has abstractyes

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