Providing A ‘Helping Hand’ To ‘Get to Know Me’ And What ‘I Can’ Do For People With Dementia
Bibliographic record
Abstract
Abstract Background In November 2020 the referral rate for residents with dementia for communication assessment was only 18% of all referrals. The Speech and Language Therapy (SLT) Department had no standard pathway for assessment and intervention of communication for those residents. Part of the aim of devising this pathway was to empower our fellow Health Care Workers (HCWs) to have meaningful, successful and satisfying conversations and facilitate positive, person-centred communication. Methods Results Qualitative feedback received from staff and families highlight our residents' “personhood” and how the tools help preserve residents' memories. The tools are available in resident's files for all HCWs to use. SLTs continue to complete the cognitive and language screens but student nurses complete the Getting To Know Me questionnaire. Since commencement of this initiative there have been more than 230 residents whom have at least 1 of the tools is completed. Conclusion These tools can help reveal the personhood of our residents and can empower all HCWs in conversation with residents. They help to provide comfort and attachment to people with dementia by helping us maintain their identity and foster inclusion by empowering residents and staff in conversations. This project is easily replicated and practical.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".