Staff, resident, and care partner perceptions on the use of a personalized tablet to mitigate the impact of isolation in long-term care residents
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Dementia Isolation Toolkit (DIT) project developed DIT-Tech, a tablet-based tool to engage residents. Implementing such technology faces challenges like digital literacy and organizational resistance. This study aimed to develop an understanding of the staff, resident and care partner experiences, including barriers and facilitators to the adoption of remote-access personal tablets in long-term care homes (LTCHs). RESEARCH DESIGN AND METHODS: Guided by the FITT framework, which emphasizes the alignment between technology, users, and clinical activities, this investigation sought to uncover the obstacles and drivers influencing the integration of DIT-Tech within the LTCH setting. Recruitment involved voluntary participation of various stakeholders within the LTCHs: 20 staff members, 23 care partners, and 7 residents who received the DIT-Tech tablets. Purposeful selection ensured representation across demographics and levels of tablet usage. Over the research period, a total of 59 in-depth interviews were conducted via telephone or video calls. Data collection and analysis occurred simultaneously. Coding strategies, incorporating both inductive and deductive approaches, were employed. RESULTS: The study highlighted pivotal factors in DIT-Tech implementation within LTCHs. Initial enthusiasm among staff and care partners was countered by staff resistance due to workload and past tech issues. Personalization benefited residents but usability challenges and poor integration posed barriers. Aligning tech with organizational goals is crucial. Privacy measures were valued. Care partners and residents embraced DIT-Tech, emphasizing the need for targeted support for staff. DISCUSSION AND IMPLICATIONS: These findings stress the necessity of robust guidelines for implementing remote-controlled tablets in LTCHs, providing vital insights for enhancing technology in care settings.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".