Coconstruction d’une intervention infirmière centrée sur la personne pour soutenir l’autogestion des individus vivant avec le diabète de type 2
Bibliographic record
Abstract
Introduction : People living with type 2 diabetes are brought to make demanding behavioral changes that can lead to self-management difficulties.Background : The guidelines recommend that healthcare professionals follow a person-centered approach (PCA) when caring. However, this approach seems difficult to adopt in practice.Objective : Coconstruct an intervention inspired by the concept map (CM) to promote the adoption of PCA by nurses during self-management support encounters.Method : This study is based on a model for developing evidence-based nursing interventions. Five experts (2 patients, 2 nurses, 1 educational specialist) collaborated in the coconstruction of the intervention.Results : The Person-Centered Approach Diabetes Self-Management Support (PCA-DSMS) intervention has been developed in accordance with the foundations of a PCA and the CM. It includes four steps : 1) Introduce the intervention ; 2) Develop the Needs Map ; 3) Intervene according to priority needs ; 4) Conclude and plan a follow-up.Discussion : More studies are needed to explore whether the intervention is acceptable and feasible as well as its ability to lead nurses to adopt PCA.Conclusion : The PCA-DSMS could bring nurses to adopt a PCA.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".