Person-centered care for older adults at nursing homes in the Iberian Peninsula: A Systematic Review
Bibliographic record
Abstract
Portugal and Spain are neighboring countries, and both have aged populations. The aging of a population leads to a higher need for gerontological care. Person-centered care for older adults (PCCOA) offers a higher standard of quality at nursing homes. This systematic review aimed to: synthesize Portuguese and Spanish studies on PCCOA; describe the process of implementation of PCCOA; explore the impact of interventions based on PCCOA and determine the measurement tools used to evaluate PCCOA. This review was conducted in accordance with the PRISMA guidelines. Eleven electronic databases were used. All of the included studies examined PCCOA at Portuguese and/or Spanish nursing homes. Thirty-one of the included studies were conducted in Spain, and six were conducted in Portugal. Studies involved the development of models/recommendations, the promotion of resident participation in care processes and education/training. Their results indicated that PCCOA has positive outcomes for staff members and residents. Six measurement tools were used in the studies conducted in Spain. This review highlights the importance of PCCOA and the need for scientific advances, especially in Portugal. The current knowledge emerging from this systematic review will guide care planning and interventions to improve care quality and has the potential to create networking opportunities.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".