Self-managing and managing self: practical and moral dilemmas in accounts of living with chronic illness
Bibliographic record
Abstract
BACKGROUND: Patient education self-management programmes draw on sociological understanding of experiencing single chronic illnesses, but health practitioners do not always recognize the tensions and ambiguities permeating individuals' management experiences, particularly for those with multiple morbidity. The aim of this study was to illuminate how people negotiate multiple chronic illness, and everyday life. METHODS: A sample of 23 people in their early 50s was recruited from a community health survey in Scotland. The participants had four or more chronic illnesses and were interviewed twice. The qualitative data that were generated highlighted the impact of illness and associated management strategies, as people attempted to continue familiar lives. Analysis was based on constant comparison and informed by a narrative approach. RESULTS: People used multiple techniques to manage symptoms and conveyed a moral obligation to manage 'well'. However, maintaining valued social roles, coherent identities and a 'normal life' were prioritized, sometimes over symptom containment. This led to tensions, and participants faced moral dilemmas as they self-managed. DISCUSSION: Self-management policies, programmes and healthcare practitioners need to recognize the tensions that people experience as they negotiate symptoms, valued social roles, positive identities, and daily life. Addressing these issues may improve opportunities to support patients in particular contexts, and enhance self-management.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.058 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| 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".