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Record W4377985626 · doi:10.56687/9781447366188-004

Accessing nursing home care: family members’ unpaid care work in Ontario and Sweden

2023· book-chapter· en· W4377985626 on OpenAlexaboutno aff
Petra Ulmanen, Ruth Lowndes, Jacqueline Choiniere

Bibliographic record

VenuePolicy Press eBooks · 2023
Typebook-chapter
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsNursingCare workWork (physics)Paid workNursing homesMedicineEngineering

Abstract

fetched live from OpenAlex

This chapter explores the unpaid work of family members with elderly relatives in the lead-up to nursing home care in two jurisdictions: Ontario, Canada, and Sweden. Unpaid work includes providing care, as well as the navigation and the advocacy work required to seek, apply for and enter nursing home care. Although Sweden has a universal social democratic approach, and Canada a selective liberal approach, both countries have seen rationing in long-term care funding and reduced access to nursing homes. In both jurisdictions, families take on extensive unpaid work and experience increasing stress leading up to nursing home admission. In Canada, after admission, families often experience a sense of guilt and continue their unpaid work in an attempt to fill care gaps. This contrasts to Sweden, where families express relief, as safety and continuity of care increase, enabling them to be visitors rather than care providers, which may reflect higher staffing levels.

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)0.001
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 score0.049
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0100.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.126
GPT teacher head0.395
Teacher spread0.269 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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