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Ethical Dilemmas in Home Care Case Management

2002· article· en· W119581836 on OpenAlexaffabout
Elaine Gallagher, Denise Alcock, Elizabeth Diem, Douglas Angus, Jennifer Medves

Bibliographic record

VenueJournal of Healthcare Management · 2002
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsCase managementBusinessNursingMedicineProcess management

Abstract

fetched live from OpenAlex

The role of case manager is fraught with challenges in a healthcare environment characterized by rapid aging of the population, a move against institutionalization of seniors, and the need to contain healthcare costs. This study examined experiences of 89 case managers through focus groups in five urban and five rural regions of Canada to identify ethical dilemmas and issues encountered in their role. Overall, the case managers expressed frustration for the lack of support for their work as evidenced by inadequate resources and few agency policies. The analysis of the focus group data revealed four main themes in relation to ethical concerns and dilemmas: (1) issues related to equity, (2) beneficence, (3) non-maleficence, and (4) autonomy and power imbalances. The situation facing these workers is grave and steps must be taken to provide them with ongoing training, support, and resources to continue in this vital role. System changes that would reduce some of the ethical conflicts experienced by case managers include funding for long-term care to keep pace with growing demands, better management of client waitlists to ensure that the most needy are given the highest priority, more supportive housing options that provide for some on-site coordination of services, better opportunities for health promotion, and better interdisciplinary teamwork so that case managers are not left making decisions in the absence of other key service providers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.403
Teacher spread0.342 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations44
Published2002
Admission routes2
Has abstractyes

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