Development of a self-scan to evaluate and improve person-centered care in nursing homes: A Delphi study
Bibliographic record
Abstract
Background and objective: Person centered care (PCC) has become the gold standard for providing care in nursing homes (NHs). Therefore, it is important for healthcare professionals in NHs to learn PCC-skills and to be supported to learn about- and improve the quality of PCC they provide. At this moment an instrument to support healthcare professionals in NHs to monitor and evaluate PCC is limited. The aim of the study was to develop a self-evaluation tool that provides healthcare professionals in NHs insight into the extent to which they provide PCC to residents, so that they can learn and further improve their current ways of working in a person-centered way.Methods: A three-round Delphi study with an expert panel (n = 25) in the domains of PCC, quality of NH care and education of caring staff. Findings were validated by residents and relatives during semi-structured interviews. Thematic analysis and descriptive statistics were used to analyze the data.Results: In the first round the experts did not provide measuring instruments, but we identified 18 key aspects of PCC. In the second round, three clusters were identified, and a scale was added, to enable assessment. In the third round, we deduplicated, restructured and used more clear language. This led to 14 key aspects of PCC, 24 measures, grouped into five clusters: knowing the resident, establishing relationship, a respectful approach, making decisions jointly and personal development. The result is a PCC self-scan for healthcare professionals in NHs. Residents and relatives, agreed with all aspects and stated that no aspects were missing.Conclusions: In this study we developed an accessible self-report learning tool for healthcare professionals that makes it possible to evaluate and improve their PCC-skills and improve the quality of PCC in NHs.
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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 | 0.126 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".