Developing an Implementation Framework for Web-Based Presence Technology Integration in Long-Term Care Homes
Bibliographic record
Abstract
Abstract Web-based presence technologies (WPT) have been recognized for their ability to mitigate social isolation and foster a sense of aging in place among older adults within long-term care homes. Despite ongoing interest in WPT use by a range of stakeholders, a guiding framework for its sustained implementation in long-term care homes is lacking. This multi-method study sought to develop an evidence-informed framework for integration of WPT within long-term care homes. Using a qualitative descriptive approach, semi-structured interviews were conducted with 12 long-term care home leadership personnel, 22 family members, 10 staff, and seven older adults. Additionally, documents such as long term-care policies and guidelines were examined. Multi-stage data analysis included: 1) directed content analysis guided by Technology Acceptance Model to examine older adults, family members, and staff experiences; 2) conventional content analysis of documents and leaders’ accounts. Findings were triangulated to develop an implementation framework highlighting factors to be considered to achieve successful WPT integration within long-term care homes. Facilitating factors included: training to enhance digital literacy of WPT end users, collaboration between long-term care homes and external companies for resource acquisition (devices, funding, human resources), and diverse applications of WPT use (i.e., social connectedness, telehealth). Potential barriers included: funding constraints and competing priorities within long-term care, lack of standardized organizational guidelines on WPT use, and usability challenges among end-users. Findings highlight the need for sustained funding opportunities, ongoing digital literacy programs for end-users, and standardized guidelines to ensure equitable and sustained integration of WPT across long-term care homes.
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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 | 0.057 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| 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".