Development and validation of the Moral Distress in Dementia Care Survey instrument
Bibliographic record
Abstract
AIMS: To report on the development and validation of the Moral Distress in Dementia Care Survey instrument. BACKGROUND: Despite growing awareness of moral distress among nurses, little is known about the moral distress experiences of nursing staff in dementia care settings. To address this gap, our research team developed a tool for measuring the frequency, severity and effects of moral distress in nursing staff working in dementia care. DESIGN: The research team employed an exploratory sequential mixed method design to generate items for the moral distress questionnaire. Data were collected between January 2013 - June 2014. In this paper, we report on the development and validation of the Moral Distress in Dementia Care Survey instrument. METHODS: The Moral Distress in Dementia Care Survey instrument was piloted with a portion of the target population prior to a broader implementation. Appropriate statistical analyses and psychometric testing were completed. RESULTS: The team collected 389 completed surveys from registered nurses, licensed practical nurses and healthcare aides, representing a 43.6% response rate across 23 sites. The Moral Distress in Dementia Care Survey emerged as a reliable and valid instrument to measure the frequency, severity and effects of moral distress for nursing staff in dementia care settings. The relative value of the Moral Distress in Dementia Care Survey as a measurement instrument was superseded by its clinical relevance for dementia care staff. CONCLUSION: The Moral Distress in Dementia Care Survey is a potentially useful tool for estimating the frequency, severity and effects of moral distress in nursing staff working in dementia care settings and for the evaluation of measures taken to mitigate moral distress.
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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 | 0.077 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".