Improving the Uptake of Independent Dialysis using the Humanbecoming Theoretical Approach
Bibliographic record
Abstract
Soaring healthcare costs, increasing rates of chronic illness, and an aging population have left Canada struggling to meet the growing demands for quality health care. Hospitals battle to cope with altering patient demand, higher costs, provincially imposed global budgets, fast developing technology, rigid rules, new drugs, and social inequalities that lead to poor health. Canadian population health trends have played an important role in examining innovation opportunities that can dictate terms for the effective re-design of Canada's health system. Independent (home) dialysis is associated with cost savings and improved quality of life in comparison with hospital-based hemodialysis treatment. Despite this, independent dialysis has failed to increase at the same rate as hospital-based treatment for chronic kidney disease. One probable reason is the lack of healthcare providers to truly understand the patient experience of living with chronic kidney disease. Qualitative data have shown that patients living with chronic kidney disease desire independence and minimal impact to their quality of life. Parse's Humanbecoming theory has been widely accepted in nursing practice as a theoretical base in which to gain an understanding of the lived experience. The values and assumptions of the Humanbecoming theory are also congruent with patient-centered care practice and transferable to all areas of healthcare practice and disciplines.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".