Two programs, too many names? A critical review of ride-sharing and safe-ride programs as alternatives to impaired driving
Bibliographic record
Abstract
INTRODUCTION: Alternative transportation programs are widely promoted as a viable strategy for prevention of alcohol-impaired driving (AID) and crashes, with ride-sharing and safe-ride being two major approaches. The scientific literature on these programs frequently uses the terms "ride-sharing" and "safe-ride" interchangeably, though their meaning is not synonymous. This critical review set out to clarify the main characteristics of these programs to advance research, dissemination of the findings, and knowledge transfer in the alternative transportation field for AID and crash prevention. METHOD: A systematic literature search of six databases using the PRISMA-S checklist identified studies of ride-sharing and safe-ride programs to prevent AID or crashes. Inclusion criteria comprised studies published in academic and gray literature between 1980 and 2023. A six-step thematic analysis of included studies identified the defining characteristics of each program. RESULTS: The 32 included studies evaluated for-profit ride-sharing/ride-hailing programs (n = 21) and safe-ride programs (n = 11). No studies on non-profit ride-sharing programs were identified. Analyses revealed two main themes. Operational strategies were most important for distinguishing between for-profit ride-sharing and safe-ride programs, with differences in these subthemes: purpose (revenue generation vs. AID reduction), management (private vs. private plus other strategies), funding (self-financing vs. external), and promotion (convenient transportation vs. dangers of AID). Service offerings, the second theme, highlighted differences in program costs, availability, accessibility, service capacity, coverage, and types of vehicles used. DISCUSSION: The scientific literature on ride-sharing was limited to for-profit ride-sharing, suggesting that referring to them as "ride-hailing" in future studies would be more accurate. Both operational strategies and service offerings highlight the advantages and disadvantages of ride-hailing and safe-ride programs in the context of AID. Some programs referred to as ride-sharing programs have the same operational strategies as safe-ride programs, suggesting these be classified as safe-ride programs for conceptual coherence.
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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 | 0.036 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".