Unpacking ‘the cloud’: a framework for implementing public health approaches to palliative care
Bibliographic record
Abstract
Designing and implementing population-based systems of care that address the social determinants of health, take action on multiple levels, and are guided by evidence-based principles is a pressing priority, and an international challenge. Aging persons are a priority demographic whose health needs span physical, psychosocial and existential care domains, increase in the last year of life, are often poorly coordinated and therefore remain unmet. Compassionate communities (CCs) are an example of a public health approach that fully addresses the holistic healthcare needs of those who are aging and nearing end of life. The sharing of resources, tools, and innovations among implementers of CCs is occurring globally. Although this can increase impact, it also generates complexity that can complicate robust evaluation. When initiating population health level projects, it is important to clearly define and organize concepts and processes that are proposed to influence the health outcomes. The Health Impact Change Model (HICM) was developed to unpack the complexities associated with the implementation and evaluation of a Canadian CC intervention. The HICM offers utility for citizens, leaders and decision-makers who are engaged in the implementation of population health level strategies or other social approaches to care, such as compassionate cities and age or dementia-friendly communities. The HICM's concepts can be adapted to address a community's healthcare context, needs, and goals for change. We share examples of how the model's major concepts have been applied in the development, evaluation and spread of a complex CC approach.
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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 | 0.119 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.011 | 0.031 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".