Process evaluation of two remedial programs for alcohol-impaired drivers
Bibliographic record
Abstract
The Alberta Motor Association (AMA) administers and delivers the Alberta Impaired Driversr Program (AIDP) under contract with Alberta Transportation. As such, it is responsible for the development, delivery, and oversight of two remedial programs for impaired drivers. The successful completion of these programs (Planning Ahead for first offenders and IMPACT for repeat offenders) is a condition of licence reinstatement for all drivers following an impaired driving conviction. This evaluation aims to identify what is currently working well within the AIDP and, more specifically, the Planning Ahead and IMPACT programs. Other goals of the evaluation are to examine program use and the effectiveness of operations, and to identify potential improvements. The approach to answer all the research questions consists of a methodology based on the collection and synthesis of both qualitative and quantitative information. The manner in which this was achieved included focus groups, program observation, analysis of quantitative participant data and the use of a Delphi panel to inform the synthesis of all the data. Many strengths of the delivery and administration of the Planning Ahead and IMPACT programs have been identified. The adopted methodology has also provided insight into some areas where improvements can be made and ways that the program can be extended to better address the needs of its target population. Recommendations based upon the outcomes of the process evaluation of Albertars remedial programs will be discussed in detail. Some recommendations can also be useful to other jurisdictions that are considering a review, updates, or modifications to their remedial impaired driver programs.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".