Implementation of New Technologies in an Aged Care Social Day Program: Mixed Methods Evaluation
Bibliographic record
Abstract
Background: Australia's aging population is looking to age in place, accessing care alternatives external to the traditional model of residential aged care facilities. This evaluation is situated in a Social Day Program, delivered by an aged care organization. It is designed to cater for people living with dementia, located in an environment equipped with new technologies including age-specific interactive computer gaming, social robots, sensory stimulation, and virtual reality. The technologies are designed to support older adults, enabling them to stay connected and maintain physical and cognitive functioning, independence, and quality of life. Objective: This project aimed to undertake a multifaceted evaluation of the implementation of the new technologies, including an exploration of the barriers and enablers to uptake. The key issue is how to enhance the potential for optimizing the use of these technologies in the Social Day Program environment, to help inform decision-making regarding the implementation of these technologies at the organization's other sites, and future investment in such technologies by aged care organizations generally. Methods: Observation of technology use within the organization was conducted over a 16-week period. Surveys and semistructured interviews were used to collect information from staff related to their experiences with the technology. Thematic analysis was used to analyze the interviews. Data were triangulated across the sample. Results: Forty-eight observation periods were completed, totaling 126.5 observation hours. Technology use by clients was observed on 24 occasions, for 22 (17.4% of the observation time) hours. Nineteen staff completed surveys. Nearly three-quarters (n=14) of the staff perceived there to be barriers to the clients' use of technology, and 18 (95%) staff reported that they assisted clients to use the technology. Ten (53%) staff reported receiving training to use the technology and feeling confident in their knowledge of the technology to assist clients in using it. Twelve staff members participated in an interview. Key themes identified from the interview data were: technology has potential but is not for everyone, incorporating the subtheme technology as a placation tool, staff knowledge and confidence, and technology functionality and support. Conclusions: This evaluation identified that technology was not being used for the purposes of enrichment or experience enhancement, nor extensively. Multiple barriers to the implementation and sustained use of the technology items were identified. Recommendations to improve implementation and promote sustained use of technology, based on the findings of this evaluation and evidence from the literature, may apply to other organizations seeking to implement these technologies in similar programs.
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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 | 0.071 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".