Bibliographic record
Abstract
In their lead paper, Lewis and Kouri leave us with a revealing and perplexing picture of Canadian experience with healthcare regionalization. Take-home messages are that regionalization is riddled with dilemmas, saddled with problems and tasked to find solutions current incentives do not encourage. And yet this mode of restructuring healthcare endures through peaks and flows of reconfiguring and renaming exercises, rarely discarded completely and apparently making some positive differences. While this peer commentary shares many of the lead authors' perspectives, it suggests that if we want to better understand the role of regionalization as a structure supporting organizational change, then there is value in broadening some investigative spaces and some analytical frames when trying to understand this seemingly endless restructuring effort. The commentary begins by arguing that we should seek transferable lessons and lenses more widely and more often, as regionalization is neither uniquely Canadian nor solely healthcare oriented. The remainder of the paper revisits some of the Lewis and Kouri terrain, viewing in turn the good, the bad and the ugly aspects of regionalization and reflecting on how these characteristics influence change opportunities both in practice and for research.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.046 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 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".