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Record W4223445190 · doi:10.1017/s0714980821000660

Balancing Flexibility and Administrative Burden: Experiences of Family Managers Using Directly Funded Home Care in Manitoba, Canada

2022· article· en· W4223445190 on OpenAlexafffundabout
Lisette Dansereau, Christine Kelly, Katie Aubrecht, Amanda Grenier, Allison Williams

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsMcMaster UniversityUniversity of TorontoSt. Francis Xavier UniversityUniversity of Manitoba
FundersInstitute of Health Services and Policy Research
KeywordsFlexibility (engineering)GerontologyBusinessNursingMedicineManagementEconomics

Abstract

fetched live from OpenAlex

Abstract Directly funded (DF) home care provides funding to home care recipients to coordinate their own care and supports, and is available across all Canadian provinces. Current research on DF home care focuses on the experiences of adults with disabilities self-directing their own care, but less is known about the experiences of family members managing services for adults 55 years of age and older. This article presents findings from a qualitative analysis of 24 semi-structured interviews with older adults and caregivers using the DF program in Manitoba, Canada, focusing on family manager experiences. We identify three themes in the interview data: (1) DF home careenhances choice and flexibilityfor older people and their caregivers, (2) choice and flexibilityreduce caregiver strain, and (3)agency services reduce administrative burden.We discuss the importance of care relationships and the role of family managers. We recommend that traditional home care systems learn from DF, and that increased administrative support would reduce caregiver strain.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.059
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0270.009
Scholarly communication0.0050.002
Open science0.0020.005
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 head0.053
GPT teacher head0.298
Teacher spread0.245 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

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Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicHealthcare innovation and challengesFrench-language works237,207