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Record W7079469772 · doi:10.26108/5p8a-7685

Getting prepared to care: understanding the experiences of caregivers in Nova Scotia

2024· other· en· W7079469772 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2024
Typeother
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsRespite careNova scotiaGovernment (linguistics)Unpaid workWork (physics)Neoliberalism (international relations)Public policyCommodificationCare work

Abstract

fetched live from OpenAlex

This thesis explores the challenges of caregivers in Nova Scotia and their barriers to accessing respite care and government support. This thesis is guided by social reproduction theory, the political economy of aging, and the life course perspective. These perspectives help to better understand how unpaid care work is valued in the care economy and why this labour has been largely overshadowed and underappreciated due to neoliberalism and capitalism. This research uses a mixed-methods approach including seven semi-structured interviews with caregivers across the province, which are supplemented with secondary data analysis of the 2018 General Social Survey – Caregiving and Care Receiving. The findings highlight the need for better access to home care services, respite care, and government funds to help caregivers and mediate caregiver burden. Helping caregivers in this manner is a necessary step to avoid a crisis of care and to ensure a better quality of life for caregivers and their care recipients.

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)0.006
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 score0.310
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.261
Teacher spread0.233 · 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
Published2024
Admission routes1
Has abstractyes

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