Understanding and Improving Quality of Care in the Context of Depressed Elderly Persons Living in Norway
Bibliographic record
Abstract
Effective leadership plays an important role in safe patient care. The aim of this paper was to understand and improve the implementation outcomes identified by empirical studies based on Proctors et al.’s key concepts, acceptability appropriateness, feasibility and fidelity, and to propose recommendations for further research. Methods: An interdisciplinary approach using mixed methods. Results: A total of twenty papers based on data from this interdisciplinary study have been published. Overall, our published empirical studies revealed that the CCM intervention had positive results due to staff members’ engagement to improve care, their awareness of the need for collaboration and willingness to assume responsibility for patient care. From the perspective of the depressed elderly persons the results of the research project indicated their need for support to increase self-management. In conclusion, an improved understanding of the implementation outcomes will have an impact on best practice for depressed elderly persons and dissemination purposes. Quality management and highly action-oriented involvement are necessary in implementation research. These will also affect the professional development of interdisciplinary teams as well as constitute a basis for further research on understanding and improving the care of depressed elderly individuals.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".