Identifying Markers of Dignity-Conserving Care in Long-Term Care: A Modified Delphi Study
Bibliographic record
Abstract
Ensuring that people living in nursing homes (NHs) are afforded with dignity in their daily lives is an essential and humane concern. Promoting dignity-conserving care is fundamentally important. By nature, however, this care is all-encompassing and holistic, and from current knowledge it is challenging to create explicit strategies for measuring dignity-conserving care. In practice the majority of current NH indicators of quality care are derived from information that is routinely collected on NH residents using the RAI-Minimum Data Set (MDS). In this regard, issues that are more tangible to resident dignity such as being treated with respect, compassion, and having opportunities to engage with others are not adequately captured in current NH quality of care indicators. An initial set of markers was created by conducting an integrative literature review of existing markers and indicators of dignity in the NH setting. A modified Delphi process was used to prioritize essential dignity-conserving care markers for use by NH providers, based on factors such as the importance to fostering a culture of dignity, the impact it may have on the residents, and how achievable it is in practice. Through this consensus building technique, we were able to develop a comprehensive set of markers that capture the range and diversity of important dignity-conserving care strategies for use in NHs. The final 10 markers were judged as having high face validity by experts in the field and have explicit implications for enhancing the provision of daily dignified care to NH residents. These markers make an important addition to the traditional quality indicators used in the NH setting and as such, bridge an important gap in addressing the psychosocial and the less easily quantified needs of NH residents.
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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 | 0.085 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".