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Record W2033108291 · doi:10.1300/j027v22n02_03

Rationing Home Care Resources: How Discharged Seniors Cope

2003· article· en· W2033108291 on OpenAlexaffabout
Georgia Livadiotakis, Gloria Gutman, Marcus J. Hollander

Bibliographic record

VenueHome Health Care Services Quarterly · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRationingFeelingNursingCoping (psychology)Home healthHealth careMedicinePopulationPersonal careSilenceBusinessGerontologyFamily medicinePsychologyEconomic growthEnvironmental healthPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Rationing home care services has become a common strategy used by state/provincial governments to control escalating health care costs, particularly at a time when very little new funding has been re-directed to the home care sector. Across British Columbia, Regional Health Authorities had implemented service reforms that call for the discharge of higher functioning clients from home support service. This paper describes the coping strategies of 137 senior clients who were discharged from home support services and from the Continuing Care Program in the Simon Fraser Health Region located in British Columbia, Canada. Personal interviews were conducted by experienced case managers to gain an understanding of how seniors were coping 19 to 21 months after their discharge. Of the 137 clients, 34.3% are characterized as being "home alone and suffering in silence," 29.2% reported receiving assistance from informal sources or reported paying out-of-pocket for private care, and 28.4% reported that they can do the work better themselves. The remaining 8.0% of participants reported mixed feelings about the impact of their discharge from home support service. The effectiveness of discharge targeted to a senior population is discussed and it is suggested that functional status together with age are important criteria when rationing home care services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.325
Teacher spread0.312 · 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 teacher head, not a consensus.

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

Citations11
Published2003
Admission routes2
Has abstractyes

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