DEMENTIA CARE MAPPING (DCM): ODDS OF IT’S USE AS AN OBSERVATORY TOOL TO MEASURE QUALITY OF CARE
Bibliographic record
Abstract
A literature review was performed to present critique and to understand use of Dementia Care Mapping (DCM) in different patient situations. The review aimed to help in decision making for choosing DCM as an assessment tool to measure care-quality based on patients’ optimal needs. Keywords and thesaurus searches were performed in Ovid interdisciplinary databases to find articles those specifically used DCM to measure quality and published between 1992 and 2016. Medical Subject Headings (MeSH) search terms “dementia care mapping” AND “quality of life” was used. Other keywords were “well-being”, “quality of care”, “wellbeing”, “quality-of-life”, “quality-of-care”, “QOL” and “DCM”. Out of sixty one yielded results, seventeen articles met inclusion criteria and were examined in this review. The critiques included inadequate sample size, sampling bias, short evaluation periods and a lack of consideration of the confounding variables commonly associated with dementia. Methods used to validate the use of DCM in different studies were highlighted. The review identified DCM has good validity and inter-rater reliability. However, content validity remains less convincing and it can only be considered as a moderately valid instrument for patients suffering from moderate to severe dementia. Although DCM is a very useful and one-of-a –kind tool for staffs who wish to improve the quality of their care, but it is a very time-consuming method and requires considerable training and attention of responders to complete the forms. It is recommended that this tool should be amended and used side-by-side of other tools to compare correlations of outcome measures.
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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 | 0.088 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.038 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".