Development of an energy management education program (“the PEP program”) for adults with end-stage renal disease
Bibliographic record
Abstract
Introduction Fatigue is a highly common symptom of end-stage renal disease, interferes with occupational engagement, and is a top research priority of patients. The objective of this project was to develop an energy management education program that would meet the needs of people with end-stage renal disease and to conduct a preliminary evaluation of the acceptability and usability of the program. Methods We used the World Health Organization’s health education planning framework to guide the intervention development process. We systematically assessed the needs of people with end-stage renal disease related to energy management education, and transformed them into program objectives. Based on these findings, we designed a program that would (a) improve occupational engagement in people with end-stage renal disease; (b) be feasible; and (c) build on existing energy management education and health education literature. Finally, we conducted qualitative interviews about the program with four key informants and conducted usability testing with five target end-users. Findings: The Personal Energy Planning program combines an established problem-solving approach with three brief web modules on energy management. Preliminary testing suggested the program was acceptable to stakeholders and was usable by the target population. Conclusions Future research should explore the effects of the program on fatigue and occupational engagement in people with end-stage renal disease.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".