Barriers and facilitators to providing rehabilitation for long-term care residents with dementia: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Rehabilitation can help long-term care (LTC) residents with dementia maintain their independence. However, many residents do not receive rehabilitation. This study aimed to identify the barriers and facilitators to providing rehabilitation for LTC residents with dementia and propose practical interventions for overcoming them. METHODS: Using a phenomenological research design, we conducted a qualitative study involving 17 privately owned LTC homes in Nova Scotia, Canada. Data were collected through individual interviews and a focus group with residents with dementia (n = 3), family members (n = 4), rehabilitation providers (n = 6), and other staff (n = 3). We analyzed the data using inductive thematic content analysis and mapped the results onto the socioecological framework and the Behaviour Change Wheel (BCW) to classify and analyze barriers and facilitators to rehabilitation. The APEASE criteria (Acceptability, Practicability, Effectiveness, Affordability, Side-effects, and Equity) in the BCW were used to identify feasible interventions and policies linked to the identified barriers and facilitators. RESULTS: Barriers at the intrapersonal level included communication difficulties, comorbidities, and lack of motivation among residents. Interpersonal factors encompassed the availability of family support and lack of interdisciplinary practice. Policy/environmental factors involved limited resources, complex admission processes, low staff ratios, and restrictive restraint policies. Enhancing communication, reducing the use of restraints, promoting interdisciplinary practice, and increasing accessibility to activity spaces and equipment will improve the provision of rehabilitation for the residents. CONCLUSION: Enhancing the capabilities, opportunities, and motivations of all actors in LTC homes can potentially minimize these barriers. Interventions such as staff training on effective communication and dementia care, promoting person-centred and meaningful activities, and improving interdisciplinary collaboration are crucial. Policy measures to improve hospital-to-LTC transitions, increase volunteer involvement, educate families and communities, and recruit more staff are recommended. Addressing these barriers through targeted interventions and policy changes can significantly improve rehabilitation provision for residents with dementia in LTC settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".