WEB-BASED PRESENCE FOR SOCIAL CONNECTEDNESS IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract The COVID-19 pandemic and resultant restrictions on in-person gathering severely disrupted the social networks of older adults living in long-term-care homes (LTCH). Furthermore, the pandemic highlighted the need for embedded interventions such as web-based technology (WBT) to support social connectedness and well-being among older adults and their families. This qualitative study sought to explore how older adults living in LTCH, their family members and staff benefit from and envision WBT to support social connectedness. Semi-structured interviews were conducted with 22 family members, seven older adults and 10 staff across three LTCH. Data were analyzed using directed content analysis informed by Technology Acceptance Model. Participants predominantly used iPads, tablets, and phones to videoconference via Zoom, Teams, Facetime, or Skype. Older adults’ sense of social connectedness benefitted from their ability to visualize faces, observe non-verbal cues, perform lip reading, and follow the lives of their families outside of LTCH using WBT over conventional phone connections. In most cases, staff facilitated WBT use due to the older adults varied digital literacy, high rates of dementia and physical or visual limitations. Some family member participants found greater utility in videoconferencing technology called Portal and Alexa Echo Show which required limited navigation by the older adult. Henceforth, LTCH must adopt and implement technology that reflects the needs of people living in LTCH. In addition, adequate staffing, training, and funding is needed to enhance and sustain WBT use for the purposes of social connection among older adults in LTCH and their families.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".