Multi-disciplinary supportive end of life care in long-term care: an integrative approach to improving end of life
Bibliographic record
Abstract
BACKGROUND: Optimal supportive end of life care for frail, older adults in long term care (LTC) homes involves symptom management, family participation, advance care plans, and organizational support. This 2-phase study aimed to combine multi-disciplinary opinions, build group consensus, and identify the top interventions needed to develop a supportive end of life care strategy for LTC. METHODS: A consensus-building approach was undertaken in 2 Phases. The first phase deployed modified Delphi questionnaires to address and transform diverse opinions into group consensus. The second phase explored and prioritized the interventions needed to develop a supportive end of life care strategy for LTC. Development of the Delphi questionnaire was based on findings from published results of physician perspectives of barriers and facilitators to optimal supportive end of life care in LTC, a literature search of palliative care models in LTC, and published results of patient, family and nursing perspectives of supportive end of life care in long term care. The second phase involved World Café Style workshop discussions. A multi-disciplinary purposive sample of individuals inclusive of physicians; staff, administrators, residents, family members, and content experts in palliative care, and researchers in geriatrics and gerontology participated in round one of the modified Delphi questionnaire. A second purposive sample derived from round one participants completed the second round of the modified Delphi questionnaire. A third purposive sample (including participants from the Delphi panel) then convened to identify the top priorities needed to develop a supportive end-of-life care strategy for LTC. RESULTS: 19 participants rated 75 statements on a 9-point Likert scale during the first round of the modified Delphi questionnaire. 11 participants (participation rate 58 %) completed the second round of the modified Delphi questionnaire and reached consensus on the inclusion of 71candidate statements. 35 multidisciplinary participants discussed the 71 statements remaining and prioritized the top clinical practice, communication, and policy interventions needed to develop a supportive end of life strategy for LTC. CONCLUSIONS: Multi-disciplinary stakeholders identified and prioritized the top interventions needed to develop a 5-point supportive end of life care strategy for LTC.
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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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".