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Record W4385728977 · doi:10.21203/rs.3.rs-3210975/v1

“You can’t die here”: An exploration of the barriers to dying-in-place for structurally vulnerable populations

2023· preprint· en· W4385728977 on OpenAlexafffundabout
Kelli Stajduhar, Melissa Giesbrecht, Ashley Mollison, Kara Whitlock, Piotr Burek, Fraser Black, Jill Gerke, Naheed Dosani, Simon Colgan

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryIsland HealthUniversity of Victoria
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanada Research ChairsMichael Smith Health Research BC
KeywordsPalliative careThematic analysisAutonomyEmotiveFocus groupFieldnotesVulnerability (computing)Assisted suicidePovertyQualitative researchPublic relationsNursingSociologyPsychologyEthnographyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Background: One measure of quality in palliative care involves ensuring people approaching the end of life are able to receive care, and ultimately die, in the places they choose. Canadian palliative care policy directives stem from this tenet of autonomy, acknowledging that most people prefer to die at home, where they feel safe and comfortable. Limited research, however, considers the lack of ‘choice’ people positioned as structurally vulnerable (e.g., experiencing extreme poverty, homelessness, substance-use/criminalization, etc.) have in regard to places of care and death, with the option of dying-in-place most often denied. Methods: Drawing from ethnographic and participatory action research data collected during two studies that took place from 2014 to 2019 in an urban centre in British Columbia, Canada, this analysis explores barriers preventing people who experience social and structural inequity the option to die-in-place. Participants include: (1) people positioned as structurally vulnerable on a palliative trajectory; (2) their informal support persons/family caregivers (e.g., street family); (3) community service providers (e.g., housing workers, medical professionals); and (4) key informants (e.g., managers, medical directors, executive directors). Data includes observational fieldnotes, focus group and interviews transcripts. Interpretive thematic analytic techniques were employed. Results: Participants on a palliative trajectory lacked access to stable, affordable, or permanent housing, yet expressed their desire to stay ‘in-place’ at the end-of-life. Analysis reveals three main barriers impeding their ‘choice’ to remain in-place at the end-of-life: (1) Misaligned perceptions of risk and safety; (2) Challenges managing pain in the context of substance use, stigma, and discrimination; and (3) Gaps between protocols, policies, and procedures for health teams. Conclusions: Common rhetoric regarding ‘choice’ in regard to preferred place of death fails to acknowledge how social and structural forces eliminates options for structurally vulnerable populations. Re-defining ‘home’ within palliative care, enhancing supports, education, and training for community care workers, integrating palliative approaches to care into the everyday work of non-health care providers, and acknowledging, valuing, and building upon existing relations of care, can help to overcome existing barriers to delivering palliative care in various settings, while increasing the opportunity for all to spend their end of life in the places that they prefer.

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)0.013
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0230.015
Scholarly communication0.0070.007
Open science0.0040.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.369
GPT teacher head0.559
Teacher spread0.190 · 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 designQualitative
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
Published2023
Admission routes3
Has abstractyes

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