Bibliographic record
Abstract
While it is anticipated that healthcare systems around the world will continue to rely heavily on family members and friends to provide unpaid care especially to meet the needs of our aging population, current assumptions and issues around caregivers need to be challenged and addressed if we are to expect their future support. This paper builds on Williams et al's assertion that many current assumptions and issues around caregivers need to be challenged and addressed if we are to expect their future support. Indeed, with the pool of available caregivers expected to actually shrink in the future, this paper therefore examines four key policy issues in greater depth that we can address to enable individuals to age in place and others to maintain and take on caregiving roles. Through the establishment of policies that support robust and longterm capacity planning; make clear what care recipients and caregivers can expect to receive in the form of government supports; appreciate the increasing diversity that is occurring among those taking on caregiving roles and those requiring care; and recognize the need to invest in strategies that combat social isolation, we may not only improve our future health and well-being but ensure we are also enabled to care for ourselves as we age.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".