Guidance for hospital and community-based health services for older adults: The senior friendly care framework
Bibliographic record
Abstract
To meet the needs of a population of older adults at risk of becoming frail in the context of known limitations to current practice, frameworks have emerged to guide health service development. Typically these frameworks have developed in the hospital sector despite the need for hospital/community sector co-development and adoption. In the present study one such framework – the Senior Friendly Hospital (SFH1) Framework – is examined with an intersectoral lens. The study included a scoping review of literature addressing system-based approaches to improving healthcare of older people as well as a modified Delphi process to incorporate these findings into an expanded framework. Qualitative analysis of the data extracted from the scoping review resulted in the identification of “senior friendly” excerpts that were charted using an apriori matrix provided by the SFH Framework. Researchers conducted thematic analysis of the excerpts to avoid redundancy and wrote statements to optimize thematic clarity. In a modified Delphi process, the statements were subsequently rated for perceived importance, clarity and fit by an intersectoral panel of experts resulting in a refined Senior Friendly Care (sfCare2) Framework comprising 31 statements and 7 guiding principles to consider when implementing improvements in the care of older adults. Finally, a panel of stakeholders were consulted for feedback on the clarity of the framework's intent and its anticipated impact on care. The sfCare Framework is now available to guide hospital and community-based health service development for older adults.
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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 | 0.083 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".