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Record W4377985596 · doi:10.56687/9781447366188-008

Family workers: the work and working conditions of families in nursing homes

2023· book-chapter· en· W4377985596 on OpenAlexaboutno aff
Christine Streeter

Bibliographic record

VenuePolicy Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsNursingNursing homesWork (physics)PsychologyMedicineEngineering

Abstract

fetched live from OpenAlex

This chapter explores the over-reliance on the unpaid work of families in nursing homes in the Canadian province of Ontario, with a focus on work processes, working conditions, and lack of worker protections and supports. Through a document scan analysis of the labour of family/friends, the chapter reveals the range of work families do, and how they are understood in organisational policies, procedures and processes, and analyses how conditions of work intersect with conditions of care. The concept of ‘family worker’, is developed to offer a lens on family contributions in nursing homes. Used in practice and alongside adequate staffing, the concept could facilitate staff collaboration with family workers in order to have the work of the latter recognised and their conditions of work improved. Such collaboration would include the development of work processes as well as means for documenting the work, while identifying the resources needed to improve conditions of work and support relations between staff and families.

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)0.000
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: none
Teacher disagreement score0.766
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.060
GPT teacher head0.315
Teacher spread0.256 · 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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