Understanding Patients' Experiences of Treatment Burden in Chronic Heart Failure Using Normalization Process Theory
Bibliographic record
Abstract
PURPOSE: Our goal was to assess the burden associated with treatment among patients living with chronic heart failure and to determine whether Normalization Process Theory (NPT) is a useful framework to help describe the components of treatment burden in these patients. METHODS: We performed a secondary analysis of qualitative interview data, using framework analysis, informed by NPT, to determine the components of patient "work." Participants were 47 patients with chronic heart failure managed in primary care in the United Kingdom who had participated in an earlier qualitative study about living with this condition. We identified and examined data that fell outside of the coding frame to determine if important concepts or ideas were being missed by using the chosen theoretical framework. RESULTS: We were able to identify and describe components of treatment burden as distinct from illness burden using the framework. Treatment burden in chronic heart failure includes the work of developing an understanding of treatments, interacting with others to organize care, attending appointments, taking medications, enacting lifestyle measures, and appraising treatments. Factors that patients reported as increasing treatment burden included too many medications and appointments, barriers to accessing services, fragmented and poorly organized care, lack of continuity, and inadequate communication between health professionals. Patient "work" that fell outside of the coding frame was exclusively emotional or spiritual in nature. CONCLUSIONS: We identified core components of treatment burden as reported by patients with chronic heart failure. The findings suggest that NPT is a theoretical framework that facilitates understanding of experiences of health care work at the individual, as well as the organizational, level. Although further exploration and patient endorsement are necessary, our findings lay the foundation for a new target for treatment and quality improvement efforts toward patient-centered care.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".