VOICES OF HEALTH CARE STAFF CARING FOR OLDER ADULT—A MULTI-NATIONAL PERSPECTIVE
Bibliographic record
Abstract
While healthcare staff often have a front line role in observing older adult patients within healthcare systems, research exploring their perspectives is often underrepresented in the literature globally. This qualitative research-focused symposium will explore views of healthcare staff in three different countries and four different healthcare settings regarding models of care, care transitions, and religious/spiritual support. First, Drs. Dupuis-Blanchard and Roes will present results of two different care delivery models. One study was conducted in Canada and the other in Germany. The aim of the Canadian study was to identify the needs of older adults living at home respectively necessary actions for care/service model development by local community long term care institutions. The aim of the German study was to understand the attitude of staff of four nursing homes toward a newly implemented care delivery model. Then Dr. Zakrajsek will describe a university-regional health system partnership that used a participatory action research approach to explore care transitions of older adult from hospital to home from the perspective of health system staff. Following this, Dr. Boucher will discuss semi-structured hospital staff interviews regarding the role for religious/spiritual support in the care of war veterans with advanced stage illness. The session will end with time for a brief discussion.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| 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".