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Record W4367849857 · doi:10.12927/hcq.2023.27055

Palliative Education and Care for the Homeless (PEACH): A Model of Outreach Palliative Care for Structurally Vulnerable Populations

2023· article· en· W4367849857 on OpenAlexaffvenue
NiCole T. Buchanan, Naheed Dosani, Andrew Bond, Donna Spaner, Alissa Tedesco, Nadine Persaud, Trevor Morey

Bibliographic record

VenueHealthcare Quarterly · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCanadian Hospice Palliative Care AssociationKensington HealthCanadian Institute for Health InformationSinai Health SystemArtificial Intelligence in Medicine (Canada)St. Michael's HospitalCancer Care Ontario
Fundersnot available
KeywordsOutreachPalliative careNursingPsychosocialHealth carePublic healthMedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

The Palliative Education and Care for the Homeless (PEACH) program comprises a community palliative care team serving some of the most complex clients in the healthcare system. Formal partnerships bring together physician, nursing, psychosocial and homecare, health and housing navigation supports. PEACH has served over 1,000 clients, leading field-defining research, medical education and public advocacy. The PEACH program demonstrates that innovation through deep interorganizational and intersectoral integration can drive value-based impact for the most complex clients, providing instructive lessons for public health system reform well beyond the margins faced by people who are unhoused. This paper describes how PEACH's unique model, critical community partnerships and research have been necessary for it to become a leader in community-based palliative care for structurally vulnerable people.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.015
Scholarly communication0.0050.004
Open science0.0020.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.127
GPT teacher head0.465
Teacher spread0.339 · 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 designTheoretical or conceptual
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

Citations12
Published2023
Admission routes2
Has abstractyes

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