Supporting the “hallway residents”: a qualitative descriptive study of staff perspectives on implementing the Namaste Care intervention in long-term care
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) settings are becoming home to an increasing number of people living with advanced or late-stage dementia. Residents living with advanced dementia represent some of society's most vulnerable and socially excluded populations and are thus at an increased risk of social isolation. A multisensory intervention tailored to this population, Namaste Care, has been developed to improve quality of life for residents living with advanced dementia in LTC homes. To date, limited research has explored the perspectives of staff in implementing the Namaste Care program with an emphasis on social inclusion of residents in Canadian LTC homes. This study aimed to describe the perspectives of LTC staff on the implementation facilitators and barriers of Namaste Care as a program to support the social inclusion of residents living with advanced dementia. METHODS: Using a qualitative descriptive design, semi-structured interviews (n = 12) and focus groups (n = 6) were conducted in two LTC homes in Southern Ontario, Canada, over a 6-month period. Convenience sampling was used to recruit LTC home staff from the two participating sites. Thematic analysis was used to analyze data. RESULTS: LTC staff (n = 46) emphasized the program's ability to recognize the unique needs of residents with advanced dementia, and also stated its potential to facilitate meaningful connections between families and residents, as well as foster care partnerships between staff and families. Findings indicated staff also perceived numerous facilitators and barriers to Namaste Care. In particular, providing staff with dedicated time for Namaste Care and implementing volunteer and family participation in the program were seen as facilitators, whereas the initial perception of the need for extra staff to deliver Namaste Care and identifying times in the day where Namaste Care was feasible for residents, families, and staff, were seen as barriers. CONCLUSIONS: LTC staff recognized the need for formalized programs like Namaste Care to address the biopsychosocial needs of residents with advanced dementia and offer positive care partnership opportunities between staff and family members. Although staffing constraints remain the largest barrier to effective implementation, staff valued the program and made suggestions to build LTC home capacity for Namaste Care.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".