Addressing the strengths and gaps in education and training for long term care staff who provide direct care to individuals living with dementia
Bibliographic record
Abstract
The number of residents living with dementia in long term care continues to grow as the prevalence of dementia in the population increases. Caring for individuals with dementia presents additional challenges for family members who may remain involved in their care and for long term care nursing staff who provide their direct care. Providing quality dementia care requires adequate staff education and training. The purpose of this study is to address the need to better educate and train staff who provide direct care to residents living with dementia in long term care. This was done by conducting a qualitative study within one long term care facility in the Winnipeg, Manitoba area. The researcher explored what training currently exists for long term care staff members in this facility who provide direct care to individuals living with dementia, what issues or gaps were perceived by staff and family members in the current provision of dementia education and training, and how the provision of education and training can be improved to provide maximum benefit to the staff, residents, and family members. For the purpose of this thesis, “long term care” refers to the care that older adults living with dementia receive who reside in nursing facilities and who can no longer be cared for in their homes or within the community. The results of this research indicated that there are areas where education and training is thriving but many areas in need of further development.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".