Understanding the links between resilience and type-2 diabetes self-management: a qualitative study in South Australia
Bibliographic record
Abstract
BACKGROUND: Research conducted by Ward, Muller, Tsourtos, et al. (Soc Sci Med 72(7):1140-1148, 2011) has led to the development of the psycho-social interactive model of resilience, which reveals the interaction between individual resilience factors (i.e. coping, confidence and self esteem) and external resilience environments (i.e. employment, supportive family environments and health promoting policies) in facilitating the development of resilience. This present study explored the utility of this model of resilience for understanding how people self-manage type-2 diabetes. METHODS: Data were collected via 14 semi-structured life-history interviews with women and men living with type-2 diabetes mellitus (T2DM). Participants varied according to socio-demographics (gender, age, education level, income) and were recruited based on their self-reported management (or lack thereof) of T2DM. RESULTS: The inter-play of internal traits and external resources with additive and subtractive resilience strategies were consistent with the psycho-social interactive model of resilience. Self-management was influenced by life history. Differences in self-management and material disadvantage were also identified. Alongside increased disadvantage are higher levels of external barriers to self-management practices. CONCLUSIONS: This paper supports the concepts of additive and subtractive resilience strategies for use with diabetes populations; providing health professionals and policy makers with an increased understanding of how to recognize and foster patient resilience for the improvement of self-care, disease management and ultimately health outcomes.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".