How Stories Can Contribute Toward Quality Improvement in Long-Term Care
Bibliographic record
Abstract
It is important to evaluate how residents, their significant others, and professional caregivers experience life in a nursing home to improve quality of care based on their needs and wishes. Narratives are a promising method to assess this experienced quality of care as they enable a rich understanding, reflection, and learning. In the Netherlands, narratives are becoming a more substantial element within the quality improvement cycle of nursing homes. The added value of using narrative methods is that they provide space to share experiences, identify dilemmas in care provision, and provide rich information for quality improvements. The use of narratives in practice, however, can also be challenging as this requires effective guidance on how to learn from this data, incorporation of the narrative method in the organizational structure, and national recognition that narrative data can also be used for accountability. In this article, 5 Dutch research institutes reflect on the importance, value, and challenges of using narratives in nursing homes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".