Quality of Care in the Last Year of Life: Adaptation and Validation of the German “Views of Informal Carers’ Evaluation of Services – Last Year of Life – Cologne”
Bibliographic record
Abstract
Abstract Background: To inform quality improvement and strengthen services provided in the last year of life, measuring quality of care is essential. For Germany, data on care experiences in the last year of life that go beyond diagnoses and care settings are still rare. Aim of this study was to develop and validate a German version of the ‘Views of Informal Carers’ Evaluation of Services – Short Form (VOICES-SF)’ suitable to assess the quality of care and services received across settings and healthcare providers in the German setting in the last year of life (VOICES-LYOL-Cologne). Methods: VOICES-SF was adapted and translated following the ‘TRAPD’ team approach. Data collected in a retrospective cross-sectional survey with bereaved relatives in the region of Cologne, Germany were used to assess validity and reliability.Results: Data from 351 bereaved relatives of adult decedents were analysed. The VOICES-LYOL-Cologne demonstrated construct validity in performing according to expected patterns, i.e. correlation of scores to care experiences and significant variability based on care settings. It further correlated with the PACIC-S9 Proxy, indicating good criterion validity. The newly added scale “subjective experiences of process and outcome of care in the last year of life” showed good internal consistency for each given care setting, except for the homecare setting. Test-retest analyses revealed no significant differences in satisfaction ratings according to the length of time since the patient’s death. Overall, our data demonstrated the feasibility of collecting patient care experiences reported by proxy-respondents across multiple care settings.Conclusions: VOICES-LYOL-Cologne is the first German instrument to analyse care experiences in the last year of life in a comprehensive manner and encourages further research in German-speaking countries. This instrument enables the comparison of quality of care between settings and may be used to inform local and national quality improvement activities.Trial registration: This study was registered in the German Clinical Trials Register (DRKS00011925; Date of registration: 13/06/2017).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".