A Qualitative Study of the Usability and Likability of a Virtual Group-Based Social Support Program for Older Adults in Residential Care
Bibliographic record
Abstract
OBJECTIVES: Despite social engagement being a robust predictor of wellbeing for older adults living in residential care, social programming ceased during the COVID-19 pandemic to abide by social distancing guidelines. Consequently, a tablet-based program called the Java Music Club-Digital (JMC-D) was developed. The JMC-D enables residents to engage with peers through discussion and singing from the safety of their own rooms. This study investigated the likability and usability of the JMC-D for older adults living in residential care and recreation staff. METHODS: Data was collected through semi-structured interviews and focus groups with seven residents and three recreation coordinators. Interviews were analyzed using thematic analysis. RESULTS: Three themes emerged: 1) Addressing a need, 2) Factors that affect virtual social connection, and 3) Usability. Interest in future participation in the JMC-D program varied depending on certain personal characteristics including comfort with computers, openness to new experiences, and existing social network. CONCLUSION: Findings suggest that the JMC-D is likeable and usable for residents and recreation coordinators. CLINICAL IMPLICATIONS: Study findings can be applied by researchers and residential care staff who wish to increase the use of virtual social programs to improve the psychosocial health of residents. Constructive feedback was incorporated into the platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".