Characteristics of self-management among patients with complex health needs: a thematic analysis review
Bibliographic record
Abstract
Objective There is a gap of knowledge among healthcare providers on characteristics of self-management among patients with chronic diseases and complex healthcare needs. Consequently, the objective of this paper was to identify characteristics of self-management among patients with chronic diseases and complex healthcare needs. Design Thematic analysis review of the literature. Methods We developed search strategies for the MEDLINE and CINAHL databases, covering the January 2000–October 2018 period. All articles in English or French addressing self-management among an adult clientele (18 years and older) with complex healthcare needs (multimorbidity, vulnerability, complexity and frequent use of health services) were included. Studies that addressed self-management of a single disease or that did not have any notion of complexity or vulnerability were excluded. A mixed thematic analysis, deductive and inductive, was performed by three evaluators as described by Miles et al . Results Twenty-one articles were included. Patients with complex healthcare needs present specific features related to self-management that can be exacerbated by deprived socioeconomic conditions. These patients must often prioritise care based on one dominant condition. They are at risk for depression, psychological distress and low self-efficacy, as well as for receiving contradictory information from healthcare providers. However, the knowledge and experiences acquired in the past in relation to their condition may help them improve their self-management skills. Conclusions This review identifies challenges to self-management for patients with complex healthcare needs, which are exacerbated in contexts of socioeconomic insecurity and proposes strategies to help healthcare providers better adapt their self-management support interventions to meet the specific needs of this vulnerable clientele.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".