Care Staff Perspectives on Using Mobile Technology to Support Communication in Long-Term Care: Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Long-term care (LTC) homes provide 24-hour care for people living with complex care needs. LTC staff assist older adults living with chronic conditions such as Alzheimer disease, related dementias, and stroke, which can cause communication disorders. In addition to the complex cognitive challenges that can impact communication, further difficulties can arise from cultural-language differences between care staff and residents. Breakdowns in caregiver-resident communication can negatively impact the delivery of person-centered care. Recent advances in mobile technology, specifically mobile devices (tablets and smartphones) and their software apps, offer innovative solutions for supporting everyday communication between care staff and residents. To date, little is known about the care staff’s perspectives on the different ways that mobile technology could be used to support communication with residents. OBJECTIVE This study aims to identify care staff’s perspectives on the different ways of using devices and apps to support everyday communication with adults living in LTC homes and the priority care areas for using mobile technology to support communication with residents. METHODS This descriptive study employed concept mapping methods to explore care staff’s perspectives about ways of using mobile technology with residents and to identify the usefulness, practicality, and probable uses of mobile technology to support communication in priority care areas. Concept mapping is an integrated mixed methods approach (qualitative and quantitative) that uses a structured process to identify priority areas for planning and evaluation. In total, 13 care staff from a single LTC home participated in this study. Concept mapping includes 2 main data collection phases: (1) statement generations through brainstorming and (2) statement structuring through sorting and rating. Brainstorming took place in person in a group session, whereas sorting and rating occurred individually after the brainstorming session. Concept mapping data were analyzed using multidimensional scaling and cluster analysis to generate numerous interpretable data maps and displays. RESULTS Participants generated 67 unique statements during the brainstorming session. Following the sorting and rating of the statements, a concept map analysis was performed. In total, 5 clusters were identified: (1) connect, (2) care management, (3) facilitate, (4) caregiving, and (5) overcoming barriers. Although all 5 clusters were rated as useful, with a mean score of 4.1 to 4.5 (Likert: 1-5), the care staff rated cluster 2 (care management) as highest on usefulness, practicality, and probable use of mobile technology to support communication in LTC. CONCLUSIONS This study provided insight into the viewpoints of care staff regarding the different ways mobile technology could be used to support caregiver-resident communication in LTC. Our findings suggest that care management, facilitating communication, and overcoming barriers are 3 priority target areas for implementing mobile health interventions to promote person-centered care and resident-centered care.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".