Hemodialysis patients' psychosocial characteristics and quality of life indicators
Bibliographic record
Abstract
This research examines Hemodialysis patients' psycho-social characteristics and predictors of quality of life and compliance at a small rural hospital. Sixty-four patients from the hemodialysis unit at Renfrew Victoria Hospital in Renfrew, Ontario, and a satellite unit at St. Francis Memorial Hospital in Barry's Bay, Ontario, were assessed using the Social Work Patient Profile, Perceived Quality of Life and Compliance Indices. Bivariate correlation and multiple regressions were conducted on psychosocial, physical and mental health variables to determine if they correlated, and could be predictors of, social worker and nurses' perceptions of patients' quality of life and compliance. Findings support multiple correlations between variables. Younger age, recreation, family support, self driving to dialysis, dementia, diabetes as the cause of chronic kidney failure (CKF), and other as the cause of CKF were significant individual predictors of social work quality of life score. Significant individual predictors for the nurses' quality of life scores were dementia, glomeruloneph, compliance, level of education, and polycystic kidney disease as the cause of CKF. The statistically significant risk factors for nurses' quality of life were lower levels of education and dementia. The four psychosocial variables that predicted compliance to treatment, suggested that there was increased compliance for patients who had recreation and family support, and increased risk factors with age and lower levels of education.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".