CAREGIVING INTERACTIONS OF FORMAL CAREGIVERS AND PERSONS WITH DEMENTIA: A SYSTEMATIC REVIEW OF MEASURES
Bibliographic record
Abstract
Abstract A psychometrically sound assessment of caregiving interactions is essential to collect valid information about the interactions, and to establish interventions to optimize the interactions in dementia care. This review aims to identify measures of caregiving interactions between formal caregivers and persons with dementia and evaluate their psychometric properties. We conducted a systematic review of studies published by February 2023 in four databases— PubMed, PsychINFO, CINHAL, and Web of Science. COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist was chosen for quality appraisal of selected studies. Our search yielded 818 scholarly records and identified five eligible instruments (published in six scholarly records). Three measures assessed interactions between formal caregivers and persons with dementia from both caregiver and person with dementia’s perspectives while one evaluated from the caregiver perspective and the other from person with dementia’s perspective. Additionally, three measures assessed interactions based on observations while other two were based on interviews. Preliminary results showed the Communication Behavior in Dementia (CODEM) as a promising measure with some excellent psychometrics but it needed further testing with larger sample and assessment of responsiveness to change. The remaining four measures (IC-Communication-SR, Coding Scheme for Mealtime Interactions, Montreal Evaluation of Communication Questionnaire for use in Long-Term Care (MECQ-LTC), and Barnards Feeding Scale) had concerns related to low internal consistency, low reliability, and lack of adequate psychometric tests. Overall, this review identified gaps in current measures, suggesting a need to establish psychometrically sound tools to assess and optimize caregiving interactions in dementia care.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".