The Taxonomy of Everyday Self-management Strategies (TEDSS): A framework derived from the literature and refined using empirical data
Bibliographic record
Abstract
OBJECTIVE: To extend our understanding of self-management by using original data and a recent concept analysis to propose a unifying framework for self-management strategies. METHODS: Longitudinal interview data with 117 people with neurological conditions were used to test a preliminary framework derived from the literature. Statements from the interviews were sorted according to the predefined categories of the preliminary framework to investigate the fit between the framework and the qualitative data. Data on frequencies of strategies complemented the qualitative analysis. RESULTS: The Taxonomy of Every Day Self-management Strategies (TEDSS) Framework includes five Goal-oriented Domains (Internal, Social Interaction, Activities, Health Behaviour and Disease Controlling), and two additional Support-oriented Domains (Process and Resource). The Support-oriented Domain strategies (such as information seeking and health navigation) are not, in and of themselves, goal focused. Instead, they underlie and support the Goal-oriented Domain strategies. Together, the seven domains create a comprehensive and unified framework for understanding how people with neurological conditions self-manage all aspects of everyday life. CONCLUSIONS: The resulting TEDSS Framework provides a taxonomy that has potential to resolve conceptual confusion within the field of self-management science. PRACTICE IMPLICATIONS: The TEDSS Framework may help to guide health service delivery and research.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".