Preliminary evidence for the impact of digital life stories about aged care residents on staff knowledge and understanding regarding those residents; A single arm trial
Bibliographic record
Abstract
AIMS AND OBJECTIVES: This study aimed to examine the impact of digital stories about aged care residents on staff knowledge and understanding regarding those residents. BACKGROUND: More than a quarter of a million older Australians live in residential aged care facilities. This living arrangement can inhibit the expression of a person's sense of identity. Without objects and cues that reflect the person's selfhood, it can be difficult for a person to express their uniqueness. Staff may not sufficiently appreciate the resident's individuality and therefore may not be able to customise care for the resident. DESIGN: This study used a single-arm trial design. METHODS: The study was conducted in four residential aged care facilities. Short digital life stories (3-4 min) of eight residents were constructed by student volunteers over 6 months. Participants (n = 53 care staff) completed a self-report measure of their knowledge and understanding of a resident before and after watching the resident's story. The study adhered to guidelines for Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) (see Appendix S1). RESULTS: Pre- and post-test scores of the measure were compared using paired samples t-tests. These scores changed significantly, showing an improvement of knowledge and understanding regarding residents. CONCLUSIONS: Watching digital life stories were associated with improvements in knowledge and understanding by staff, and hence have the potential to foster a greater level of understanding of residents by such staff, and more person-centred care practices within residential aged care facilities. RELEVANCE TO CLINICAL PRACTICE: Digital stories about aged care residents are quick and efficient methods for improving aged care staff members' knowledge and understanding of the residents under their care. With such understanding, staff may be able to better customise care for residents, thereby validating residents' sense of identity and elevating residents' quality of life.
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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 | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".