Using the Self-Management Assessment Scale for Screening Support Needs in Type 2 Diabetes: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Globally, most countries face a common challenge by moving toward a population-based structure with an increasing number of older people living with chronic conditions such as type 2 diabetes. This creates a considerable burden on health care services. The use of digital tools to tackle health care challenges established views on traditional nursing, based on face-to-face meetings. Self-management is considered a key component of chronic care and can be defined as management of the day-to-day impact of a condition, something that is often a lifelong task. The use of a screening instrument, such as the Self-Management Assessment Scale (SMASc), offers the potential to guide primary health care nurses into person-centered self-management support, which in turn can help people strengthen their empowerment and self-management capabilities. However, research on self-management screening instruments is sparse, and no research on nurses' experiences using a digitalized scale for measuring patients' needs for self-management support in primary health care settings has been found. OBJECTIVE: This paper describes diabetes specialist nurses' (DSNs) experiences of a pilot implementation of the SMASc instrument as the basis for person-centered digital self-management support. METHODS: This qualitative study is based on observations and interviews analyzed using qualitative content analysis. RESULTS: From the perspectives of DSNs, the SMASc instrument offers insights that contribute to strengthened self-management support for people with type 2 diabetes by providing a new way of thinking and acting on the patient's term. Furthermore, the SMASc was seen as a screening instrument with good potential that embraces more than medical issues; it contributed to strengthening person-centered self-management support, and the instrument was considered to lead both parts, that is, DSNs and patients, to develop together through collaboration. CONCLUSIONS: Person-centered care is advocated as a model for good clinical practice; however, this is not always complied with. Screening instruments, such as the SMASc, may empower both nurses and patients with type 2 diabetes with more personalized care. Using a screening instrument in a patient meeting may also contribute to a role change in the work and practice of DSNs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".