ONE VOICE—SAFER CANADA: MERGING CANADA'S FOUR LARGEST INJURY PREVENTION ORGANISATIONS
Bibliographic record
Abstract
Background Preventable injury kills more than 13 000 Canadians every year, or about 1.5 every hour. In addition to the devastating collateral damage to the person injured and their families the impact for all injury on Canada's economy is more than $19.8 billion each year. Aims/Objectives/Purpose In January 2009, the leaders from four Canadian injury prevention organisations, Safe Communities Canada, Safe Kids Canada, SMARTRISK, and Think First Canada, began to discuss ways to collaborate to advance their collective mission. Methods A multi-phase study was conducted to examine how the four organisations might jointly identify, integrate, and approve shared initiatives in knowledge management, stakeholder engagement, fund development, and marketing—to speak with one voice. Results/Outcome The result of this research was a report, ‘One Voice—Safer Canada’. The key learning was that, at present, injury is a disease without an identity, largely because, unlike other major health causes, there is no single, dominant voice in Canada working to create it. Seven specific recommendations formed the core of this report. A transition steering committee composed of senior decision makers for the organisations crafted a plan to successfully integrate their operations into one entity. Significance/Contribution to the Field As of 1 July 2012 the organisations officially merged as Parachute, with the goal of becoming leaders in the field, educating, inspiring and mobilising Canadians to prevent injuries. The merger presents a unique opportunity to enrich programming, strengthen research, and increase strategic influence to be an impactful and resourceful organisation for all Canadians.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".