Bibliographic record
Abstract
The focus of this commentary is on the relevance of the Canadian experience for developing countries. It highlights the growing urgency poor countries face in preserving their major human and social capital--community solidarity and family care. Developing countries face a double burden of disease--communicable and non-communicable diseases alike, with very few, and often shrinking, resources. While poorer countries will be able to learn about the essential elements of home-based care from the examples of Canada and other industrialized countries, they do need to develop their own systems based upon their economic, social, political and cultural realities. The primary health care system would seem to provide a foundation for the provision of long-term care on a sustainable and cost-effective basis. In contrast to the often-prevailing practice in developed countries, home-based care services could be integrated into the overall health and social system. Functional disability, regardless of disease aetiology or age of the care recipient, as well as the needs of family caregivers would thus become the defining elements of service eligibility. While the question remains open as to how much poor countries can learn from the experience of others, developing countries do have the opportunity to initiate a rational process where they first provide support to communities and informal caregivers and help to maintain patients in their homes and only later develop other service elements.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".