Bibliographic record
Abstract
You know that you're Canadian (or, presumably, Australian, Russian or Brazilian) if you measure distance in hours, not miles, or so the saying goes. The vast distances in sparsely populated landscapes represent substantial challenges for the effective delivery of high-quality care. Resources, specialized care and connections that are readily available in urban areas are in short supply. But kayaks, snowmobiles and canola are by no means the only innovations to be inspired by the needs of rural Canada. Many creative solutions to healthcare challenges have also emerged beyond big cities. New models of team-based collaborative care, expanded scopes of practice and imaginative uses of telehealth technology to deliver specialized care are just a few examples. There are rich opportunities for sharing innovations and spreading best practices from community to community. Over time, a number of these innovations have also influenced care in urban settings. For instance, telehealth, once seen as a tool to be used in remote areas, is increasingly common in communities of all sizes, sharing specialized expertise between city-centre hospitals, helping those who are homebound to get the care that they need and more. This issue of Healthcare Policy/Politiques de Sante has a number of papers that focus on health in rural settings, but their findings are relevant in a range of contexts and challenges – access to care, appropriate program planning and effective health human resources management. Other papers are also broad-based, with findings that can be applied across a number of settings. Michael Law discusses the implications of approaches to generic drug pricing; Clare Liddy and colleagues address practice facilitation programs and Mohammed Al-Hamdani presents the results of new research into plain packaging for cigarettes, to name just a few. I hope that you will find much food for thought, as well as inspiration for policy and practice improvement, in the journal's pages.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".