Using the ecological framework to identify barriers and enablers to implementing Namaste Care in Canada’s long-term care system
Bibliographic record
Abstract
BACKGROUND: Higher acuity of care at the time of admission to long-term care (LTC) is resulting in a shorter period to time of death, yet most LTC homes in Canada do not have formalized approaches to palliative care. Namaste Care is a palliative care approach specifically tailored to persons with advanced cognitive impairment who are living in LTC. The purpose of this study was to employ the ecological framework to identify barriers and enablers to an implementation of Namaste Care. METHODS: Six group interviews were conducted with families, unlicensed staff, and licensed staff at two Canadian LTC homes that were planning to implement Namaste Care. None of the interviewees had prior experience implementing Namaste Care. The resulting qualitative data were analyzed using a template organizing approach. RESULTS: We found that the strongest implementation enablers were positive perceptions of need for the program, benefits of the program, and fit within a resident-centred or palliative approach to care. Barriers included a generally low resource base for LTC, the need to adjust highly developed routines to accommodate the program, and reliance on a casual work force. CONCLUSIONS: We conclude that within the Canadian LTC system, positive perceptions of Namaste Care are tempered by concerns about organizational capacity to support new programming.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".