Factors influencing self-management in patients with type 2 diabetes in general practice: a qualitative study
Bibliographic record
Abstract
Many Australian adults with type 2 diabetes mellitus (T2DM) do not follow recommended self-management behaviours that could prevent or delay complications. This exploratory study aimed to investigate the factors influencing self-management of T2DM in general practice. Semi-structured qualitative interviews were conducted with patients with T2DM (n = 10) and their GPs (n = 4) and practice nurses (n = 3) in a low socioeconomic area of Sydney, New South Wales, Australia. The interviews were analysed thematically using the socio-ecological model as a framework for coding. Additional themes were derived inductively based on the explicitly stated meaning of the text. Factors influencing self-management occurred on four levels of the socio-ecological model: individual (e-health literacy, motivation, time constraints); interpersonal (family and friends, T2DM education, patient-provider relationship); organisational (affordability, multidisciplinary care); and community levels (culture, self-management resources). Multi-level strategies are needed to address this wide range of factors that are beyond the scope of single services or organisations. These could include tailoring health education and resources to e-health literacy and culture; attention to social networks and the patient-provider relationship; and facilitating access to affordable on-site allied health services.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".