Optimising mobility through the sit‐to‐stand activity for older people living in residential care facilities: A qualitative interview study of healthcare aide experiences
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore the experience of HCAs encouraging residents living in residential care to complete the sit-to-stand activity and to identify the strategies HCAs used to integrate the activity into their daily work routines. BACKGROUND: Decreased mobility in advanced ageing is further reduced when entering a residential care facility. Interventions such as the sit-to-stand activity have been shown to have a positive effect on the mobility of older people. There is evidence to suggest that healthcare aides are able to support residents to complete the sit-to-stand activity as part of their daily work routines; however, little is known about how healthcare aides actually do this with residents living in residential care. DESIGN: A qualitative interview study included seven purposively sampled HCAs working in residential care facilities. Semistructured interviews were analysed using inductive qualitative content analysis. RESULTS: The HCAs' experience with the sit-to-stand activity was represented by the following four categories: Resident participation, Feeling misunderstood and disrespected, Time and workload, and Management involvement. HCAs identified three strategies to help them support residents to complete the sit-to-stand activity: Motivating residents, Completing activity in a group and Using time management skills. CONCLUSIONS: HCAs reported some encouragement from managers and cooperation from residents to complete the sit-to-stand activity with residents; however, they also felt constrained by time limitations and workload demands and they felt misunderstood and disrespected. HCAs were able to identify several strategies that helped them to integrate the sit-to-stand activity into their daily routines. IMPLICATIONS FOR PRACTICE: This study highlights the challenges and supportive factors of implementing the sit-to-stand activity into the daily work routine of HCAs. The study also identifies the strategic role of nurse managers when implementing interventions in residential care facilities.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".