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Record W2083884700 · doi:10.1155/2014/529681

What Proportion of Terminally Ill and Dying People Require Specialist Palliative Care Services?

2014· article· en· W2083884700 on OpenAlexaffabout
Donna M. Wilson, Boris Woytowich

Bibliographic record

VenueInternational Journal of Palliative Care · 2014
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPalliative careTerminally illMedicineNursingEnd-of-life carePopulationFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

Currently, around 55 million people die each year worldwide. That number is expected to increase rapidly with accelerating population aging. Despite growth in the number of palliative care specialists and specialist services in most countries, the prospect of an increasing number of terminally ill and dying persons is daunting. This paper attempts to answer the question: what proportion of terminally ill and dying persons require specialist palliative care services? To address this question and highlight which persons require specialist palliative care, the current state of access to specialist palliative care services and specialists in Canada and other countries is highlighted, along with available evidence-based information on specialist services utilization and the care needs of terminally ill and dying persons. Current evidence and information gaps reveal that this question cannot be answered now, but it should be answered in advance of a crisis of unmet end-of-life care needs with the rising death toll.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.410
Teacher spread0.351 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

Explore more

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