Developing an Education Intervention for Staff Supporting Persons With an Intellectual Disability and Advanced Dementia
Bibliographic record
Abstract
Abstract Generally, staff working in settings that provide care for adults with intellectual disabilities (ID) have not received specific education with respect to extended care for terminal illnesses or late‐stage dementia. Equally, staff working in specialist palliative care often are not familiar with the unique issues of supporting persons with intellectual disabilities affected by dementia. To understand care concerns with respect to supporting persons with ID and advanced dementia, and to develop, deliver, and evaluate an educational intervention with staff in ID settings and specialist palliative care services, 14 focus group interviews were conducted with staff across six ID services and one specialist palliative care provider in the greater Dublin (Ireland) area. Qualitative descriptive analysis resulted in the emergence of key themes and formed the development of an educational intervention. Pre‐ and posttest questionnaires assessed responses to a pilot delivery of the educational intervention. Formal feedback from staff indicated that the educational intervention was highly valued and addressed key training concerns. They agreed that the training supported “aging in place,” and the preparation for a “good death” including support for staff, peers, and family in their grief and bereavement. An educational intervention in the form of a trainer manual was produced to support cross‐service system in‐service training on issues of addressing advanced dementia in persons with ID.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".