Fragmented care and whole-person illness: Decision-making for people with chronic end-stage kidney disease
Bibliographic record
Abstract
PURPOSE: The study reported herein sought to better understand how patients with multi-morbid, chronic illness-who receive care in institutions designed for treatment of acute illness-experience and engage in health-related decisions. METHODS: In an urban Canadian teaching hospital, we studied the interactions of six hemodialysis patients and 11 of the health professionals involved in their care. For 1 year (September 2009 to September 2010), we conducted ethnographic observation and interviews of six cases each comprising one hemodialysis patient and various health professionals including medical specialists, nurses, a social worker, and a dietician. RESULTS: We found that the ubiquity and complexity of health-related decision-making in the lives of these patients suggests the need for a more holistic interpretation of health-related decision-making. DISCUSSION: We propose an interpretation of decision-making as an ongoing process of integrating illness and life; as frequently open-ended, cumulative, and relational; and as fundamentally shaped by the fragmented delivery of care for patients with multiple morbidities. CONCLUSION: Our understanding of decision-making suggests that people living with complex chronic illness need to receive care from institutions that recognize and address their multi-morbidity as a whole illness that is constantly being integrated into the life of a whole person.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".