Ethical Dilemmas in Home Care Case Management
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.186 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.027 | 0.039 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".