Managing everyday life: Self-management strategies people use to live well with neurological conditions
Bibliographic record
Abstract
OBJECTIVE: This paper uses the Taxonomy of Everyday Self-management Strategies (TEDSS) to provide insight and understanding into the complex and interdependent self-management strategies people with neurological conditions use to manage everyday life. METHODS: As part of a national Canadian study, structured telephone interviews were conducted monthly for eleven months, with 117 people living with one or more neurological conditions. Answers to five open-ended questions were analyzed using qualitative content analysis. A total of 7236 statements were analyzed. RESULTS: Findings are presented in two overarching patterns: 1) self-management pervades all aspects of life, and 2) self-management is a chain of decisions and behaviours. Participants emphasized management of daily activities and social relationships as important to maintaining meaning in their lives. CONCLUSION: Managing everyday life with a neurological condition includes a wide range of diverse strategies that often interact and complement each other. Some people need to intentionally manage every aspect of everyday life. PRACTICE IMPLICATIONS: For people living with neurological conditions, there is a need for health providers and systems to go beyond standard advice for self-management. Self-management support is best tailored to each individual, their life context and the realities of their illness trajectory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".