The Problems on Your Desk - A Research Study to Define and Describe Paramedic Practice in Canada
Bibliographic record
Abstract
Paramedicine is a young discipline, with modern ambulance services emerging in Canada in 1960s. Since then, the profession has evolved into a wide range of practice settings, moving beyond emergency care and transportation of the sick and injured to providing a broad spectrum of care. Yet, across Canada, paramedicine has developed unevenly, as varied models and practices have emerged to meet the differing needs in each province and territory. The range of concepts and terms across the country make it difficult to describe and compare systems, and there is little aggregated data at a national level. This study, funded by the Canadian Safety & Security Program, seeks to develop a framework and set of national standards for describing paramedics, their practice settings, their patients, and the communities that they serve. In the first year, an applied research study will build u201cuser case scenariosu201d that explore the data needs of key stakeholder groups based on u201cthe problems on your desk.u201d These scenarios will guide the development of a conceptual framework, including core concepts, terms, definitions, taxonomies and data models describing paramedicine in Canada. This framework will form the foundation for the second phase of the project: developing a set of national standards that will be the basis of a future national Canadian Paramedic Information System. The presentation will describe the work to date and emerging findings from the study.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".