Occupational Priorities of People on Hemodialysis Who Participated in Energy Management Education
Bibliographic record
Abstract
Background. People with kidney failure who undergo hemodialysis treatment and experience chronic fatigue identify negative effects on occupational performance and participation as a key aspect of their illness experience. Purpose. To describe the occupational performance and participation problems of people treated with hemodialysis who live with debilitating fatigue. Method. Fifteen participants, who were randomized to participate in an energy management intervention as part of a randomized controlled trial, completed two occupation-based assessments at baseline and chose three priority occupational performance or participation problems to address as goals during the intervention. Results were analyzed using descriptive statistics (counts and percentages). Findings. Fifteen participants (mean age 60, 53% male) completed the occupation-based assessments. Participants stated that they wanted or needed more energy for a median of 22 of 55 occupations. Going out for food/drinks ( n = 11), going to a movie/concert/performance ( n = 10), and food preparation/clean-up ( n = 10) were the top occupations for which participants required more energy. Prioritized occupational performance and participation problems most often fell within the household management (14 goals), self-care (6 goals), and hobbies (5 goals) domains. Conclusion. Occupational performance and participation problems are extensive among people treated with hemodialysis who live with debilitating fatigue. There is a clear need for occupation-based interventions that optimize occupational performance and participation in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".