Acceptability of a Personal Contact Intervention among People Living with Dementia: Might Baseline Contact Matter?
Bibliographic record
Abstract
Abstract Our study aimed to explore how perceived baseline contact may influence acceptability of Connecting Today, a personal contact intervention, among people living with dementia. We aimed to generate hypotheses for testing in future studies. This was a sub-group analysis of pilot study data. Fifteen people living with mild to moderate dementia participated in Connecting Today. We explored how perceptions of intervention acceptability may differ in groups reporting weekly contact (n = 8) compared with groups reporting monthly/unknown (n = 7) contact at baseline. Measures of acceptability included a treatment perceptions and preferences questionnaire, and the number of and reasons for non-consent, missing data, and study withdrawal. We used descriptive statistics and content analysis. In visits one and two, a larger proportion (85.7–100%) of low baseline contact participants reported feeling better, and indicated that the visits helped them and were easy “mostly” or “a lot”, compared with the high baseline contact group (37.5–62.5%). Most missing data (71%) and all study withdrawals occurred in the high baseline contact group. Scheduled in-person visits with family, friends, or a volunteer may appeal to residents in care homes who have few existing opportunities for routine, one-on-one visits with others. Hypotheses generated should be tested in future studies.
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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 | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".