Coping, adapting or self‐managing – what is the difference? A concept review based on the neurological literature
Bibliographic record
Abstract
AIM: The aim of this study was to report: (1) an analysis of the concepts of coping, adaptation and self-management in the context of managing a neurological condition; and (2) the overlap between the concepts. BACKGROUND: The three concepts are often confused or used interchangeably. Understanding similarities and differences between concepts will avoid misunderstandings in care. The varied and often unpredictable symptoms and degenerative nature of neurological conditions make this an ideal population in which to examine the concepts. DESIGN: Concept analysis. DATA SOURCES: Articles were extracted from a large literature review about living with a neurological condition. The original searches were conducted using SCOPUS, EMBASE, CINAHL and Psych INFO. Seventy-seven articles met the inclusion criteria of: (1) original article concerning coping, adaptation or self-management of a neurological condition; (2) written in English; and (3) published between 1999-2011. METHODS: The concepts were examined according to Morse's concept analysis method; structural elements were then compared. RESULTS: Coping and adaptation to a neurological condition showed statistically significant overlap with a common focus on internal management. In contrast, self-management appears to focus on disease-controlling and health-related management strategies. Coping appears to be the most mature concept, whereas self-management is least coherent in definition and application. CONCLUSION: All three concepts are relevant for people with neurological conditions. Healthcare teams need to be cautious when using these terms to avoid miscommunication and to ensure clients have access to all needed interventions. Viewing the three concepts as a complex whole may be more aligned with client experience.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".