Evaluation of the implementation of advanced nursing competencies in the Basque health care system
Bibliographic record
Abstract
In 2011 advanced competences in nursing were defined and pilot tested in the Basque Healthcare System, in order to meet the needs of chronic patients. It is assumed that nursing professionals, in a functional sense, can fulfil a liaison role both within the health system and with external stakeholders. Integration between levels of care, the mobilisation of resources and case management are fundamental factors in achieving this objective. Background: In 2010, an overall strategy for tackling the challenge of chronicity was launched in the Basque Country. Its main objective was to drive the Basque Health Service (Osakidetza) towards improving care for patients with chronic illnesses (Department of Health and Consumer Affairs, Basque Government and Osakidetza, 2010). Under this strategy, there was a commitment to develop and implement advanced competencies in nursing, with the objective of introducing nursing roles to better meet the needs of chronic patients. Methods: To achieve this objective, a one-group pre-test and post-test pre-experimental design was adopted for this study. We used the SATISFAD questionnaire to assess the satisfaction of patients and caregivers, and the SF-12 and Barthel Index to measure quality of life and level of independence respectively. Results: The experience of introducing the new nursing competencies has been rated as very positive by the participating patients and those around them (their caregivers and families) as it is perceived to have resulted in care that is more personalised, better planned and focused on the patient than traditional healthcare. Nevertheless, the process was not found to have significantly improved patient perception of quality of life and level of independence. Conclusions: The implementation of advanced competencies in the Basque Country has shown that case management leads to improvements in social and health care for patients, and their caregivers and families, compared to traditional care.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".