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Record W4237177884 · doi:10.32920/ryerson.14663214.v1

Allowing service users to die at home: palliative care for vulnerably housed and homeless individuals

2021· preprint· en· W4237177884 on OpenAlexaffabout
Taggart Archer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsTrent UniversityToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsPalliative careNursingHealth careResistance (ecology)Embodied cognitionService (business)Work (physics)Qualitative researchGerontologyMedicinePsychologySociologyPolitical scienceBusinessEngineering

Abstract

fetched live from OpenAlex

The current climate of death, dying and access to care is evolving on a daily basis. As community resources and shelters within Toronto are being revitalized, the demand on the healthcare system continues to increase. This study explores how an interdisciplinary community healthcare team is challenging the current model of palliative care for service users who are vulnerably housed or experiencing homelessness within Toronto. Specifically, I am looking at understanding strategies of resistance to receiving a one size fits all form of care. This qualitative design used interviews to speak with five healthcare workers who work within an interdisciplinary care team to support this population. The outcome of the study highlights the experience of the participants regarding the importance of an interdisciplinary team approach to care, the resistance embodied within practice and the field, barriers to care and the challenges of the role.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.007
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.129
GPT teacher head0.409
Teacher spread0.281 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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