Functional Status and Engagement in Physical Activity Among Maintenance Dialysis Patients: A Mixed Methods Study
Bibliographic record
Abstract
Rationale & Objective: People receiving maintenance hemodialysis (HD) experience significant activity barriers but desire the ability to do more and remain independent. To learn about how to help people who require dialysis stay active, a mixed methods study was designed to assess functional status and explore participants' lived activity experiences. Study Design: A concurrent mixed methods design was chosen to increase understanding of the real-life activity experiences of people who require dialysis through in-depth interviews paired with functional status measures. The qualitative findings were fully integrated with the quantitative results to link characteristics associated with different physical activity levels. Setting & Participants: A purposive sample of 15 adult patients receiving maintenance HD for at least 3 months was recruited from 7 dialysis centers in Newark, New Jersey. Analytical Approach: Thematic analysis using principles of interpretive phenomenology. Fully integrated quantitative and qualitative data with joint displays and conversion mixed methods. Results: Participants had a median age of 58 years and were predominantly African American (83%) and men (67%). Three descriptive categories were generated about the participants. They described physical activity as a routine daily activity rather than structured exercise. All participants experienced substantial hardship in addition to chronic kidney disease and expressed that family, friends, and faith were essential to their ability to be active. An overarching theme was generated for participants' mindsets about physical activity. Within the mindset theme, we discerned 3 subthemes comprising characteristics of participants' mindsets by levels of engagement in physical activity. Limitations: While code saturation and trends in functional status measures were achieved with 15 participants, a larger sample size would allow for deeper meaning saturation and statistical inference. Conclusions: Patients receiving maintenance HD with an engaged mindset exhibited more adaptive coping skills, moved more, wanted to help others, and had a normal body weight habitus. These participants employed adaptive coping skills to carry out daily life activities of importance, highlighting the value of adaptive coping to help overcome the challenges of being physically active.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".