Caring ahead: Mixed methods development of a questionnaire to measure caregiver preparedness for end-of-life with dementia
Bibliographic record
Abstract
BACKGROUND: Family caregivers of persons with dementia often feel unprepared for end-of-life and preparedness predicts caregiver outcomes in bereavement. Existing questionnaires assessing preparedness have limitations. A multi-dimensional questionnaire assessing family caregiver preparedness for the end-of-life of persons with dementia is needed to identify caregivers at risk for negative outcomes in bereavement and evaluate the quality of strategies within a palliative approach. AIM: To develop a multi-dimensional questionnaire titled 'Caring Ahead' to assess feelings of preparedness for end-of-life in family caregivers of persons with dementia. DESIGN: A mixed methods, sequential design employed semi-structured interviews, a Delphi-survey and pilot-testing of the questionnaire, June 2018 to July 2019. SETTING/POPULATION: Participants included five current and 16 bereaved family caregivers of persons with symptoms advanced dementia from long-term care homes in Ontario, Canada; and 12 professional experts from clinical and academic settings in Canada, Europe, United States. RESULTS: Interviews generated three core concepts and 114 indicators of preparedness sampling cognitive, affective and behavioural traits in four domains (i.e., medical, psychosocial, spiritual, practical). Indicators were translated and reduced to a pool of 73 potential questionnaire items. 30-items were selected to create the 'Caring Ahead' preparedness questionnaire through a Delphi-survey. Items were revised through a pilot-test with cognitive interviewing. CONCLUSIONS: Family caregivers' feelings of preparedness for end-of-life need to be assessed and the quality of strategies within a palliative approach evaluated. Future psychometric testing of the Caring Ahead questionnaire will evaluate evidence for validity and reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".