BUILDING OF A FRAMEWORK FOR THE IMPLEMENATION OF AN INJURY PREVENTION STRATEGY: AN ALBERTA, CANADA EXAMPLE
Bibliographic record
Abstract
Background The Alberta injury death rate per 100 000 has remained unchanged from 1999 (47.1) to 2008 (48.3). The Alberta Injury Control Strategy (AICS) was developed to address this need. Aims/Objectives/Purpose The purpose was to develop an implementation plan for the AICS. The Plan aims to accomplish results these areas: engage Albertans, to make the injury issue a priority and reduce the frequency and severity of injuries. Methods A Steering Committee comprised of top bureaucrats from 13 ministries within the Alberta Government was created to oversee the development and execution of the implementation plan for the AICS. Results/Outcome A framework was developed identifying actions to be taken across all ministries within the Provincial government. Three types of actions were identified. Core actions—essential actions to be taken to achieve the intended outcomes. Supporting actions—integral to all injury prevention initiatives and supporting the outcomes for each of the core actions. Promoting actions—actions that promote initiatives that are already underway. Significance/Contribution to the Field This document provides a template for use by all stakeholders involved in injury prevention regardless of the type of injury. Although specific injury priorities are identified, it is intended that all organisations, communities and individuals will be able to identify actions they can take to reduce injuries.
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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".