Virtual connection between older people with dementia and families during COVID-19 pandemic
Bibliographic record
Abstract
Abstract People staying in hospitals need more support to cope with the lockdown and visitor restriction during the COVID-19 pandemic, especially for older people with cognitive or physical impairment. Everyday technology such as a touchscreen tablet has great potential to support person-centred care. We aimed to support the adoption of tablets for hospitalized people with dementia to connect with families and friends. A patient-oriented research approach was employed to co-produce the toolkit. We are a transdisciplinary team, including a medical student, physicians, nurses, patients, and family partners. We facilitated staff focus groups (n=3), and conducted stakeholders’ interviews (n=4) to gain a more comprehensive understanding of users’ needs. The sample included ten patients, ten family members, 40 staff members, nurses, care workers, physicians, and unit clerks (n=40). The Consolidated Framework for Implementation Research (CFIR) guided the research design and qualitative analysis. A toolkit was developed based on participants’ perspectives on what needs to be in place to support successful adoption. We developed a mobile tablet with one mechanical arm and one leg on wheels. Participants reported impacts: (a) it puts a smile on the patient’s face, (b) it alleviates anxiety and worries on both sides, and (c) it reduces responsive behaviors. The conceptual framework CFIR provides helpful guidance in identifying barriers to implementation. Working with users including patient and family partners to explore possible solutions was key to our success. Future research should engage patient and family partners to seek proactive strategies to address obstacles to advance the science of technology implementation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".