Development and Evaluation of the Caring Ahead: Preparing for End of Life in a Dementia Questionnaire
Bibliographic record
Abstract
Abstract A palliative approach is recommended in long-term care to support persons with dementia and help families prepare for end-of-life. Despite this, 50% of family caregivers of persons with dementia report feeling unprepared for death. A questionnaire is needed to assess caregiver death preparedness as an outcome measure for strategies within palliative care. A mixed methods design with qualitative and quantitative phases was used to develop and evaluate the ‘Caring Ahead: Preparing for End-of-Life in Dementia’ questionnaire. The questionnaire has 30 items organized into Medical, Relationship/Personal, Spiritual and Practical subscales with a 7-pt Likert response scale. To date, the questionnaire has been tested with 117 participants who are 61 years old on average, female (86%), adult children (77%) caring for a person with dementia in long-term care. The mean item score is 5.61 (SD 0.71). Participants report limitations in preparedness related to: 1) communication with healthcare providers about traditions and preferences for end-of-life care; 2) knowledge of the dying process and; 3) life purpose after death. A test-retest with 32 participants demonstrates a high degree of reliability; Intraclass Correlation Coefficient 0.91 (CI95%: 0.31-0.97). A moderate positive correlation between participant total scores and a single global preparedness item suggests concurrent validity, r=.66 (CI95%: 0.51-0.80). These findings will be used to refine the questionnaire and contribute a valuable measurement tool for clinicians, researchers and policy-makers working in palliative 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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".