Re: Making Health and Care Systems Fit for and Ageing Population. Why We Wrote It, Who We Wrote It For, and How Relevant It Might Be to Canada
Bibliographic record
Abstract
In response to the commentary((1)) in this month's Canadian Geriatrics Journal by Andrew and Rockwood on the recent paper I co-wrote with King's Fund colleagues-"Making Health and Care Systems Fit for an Ageing Population"((2))-I wanted to pen a very personal response, not least because of my visits to health systems in Ontario and Alberta and conversations with many Canadian colleagues that are fresh in my mind. The paper was certainly the most important and influential thing I have written, and was an attempt to weave all the elements of good practice in health care for older people into one overarching narrative. Whilst its biggest target audience is UK health services, I hope it has some relevance to Canada and might stimulate some constructive conversations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.036 | 0.032 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.014 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 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".