Facilitator perspectives on in-person versus videoconference delivery of a remedial intervention for impaired drivers: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Remedial education programs for drivers who have committed an impaired driving offence have been adopted in many jurisdictions worldwide to address impaired driving recidivism. Back on Track (BOT) is a three-part program in Ontario, Canada, which includes an 8-hour or 16-hour workshop. Although originally mandated by the Province to be delivered in-person only, a shift to online workshop delivery was required during the COVID-19 pandemic, when public health measures forbid public gathering. This study aimed to identify: (1) benefits and drawbacks for impaired driving offenders attending the program via videoconferencing technology, and; (2) potential improvements for videoconferencing-based delivery, from the facilitators’ perspective. METHODS: Semi-structured interviews were conducted with ten BOT facilitators who had experience delivering the 8-hour workshop both in-person (before the pandemic) and online via videoconferencing. Interviews were conducted via Webex, were audio-recorded, transcribed, and thematically analyzed. RESULTS: Facilitators noted that online delivery of BOT improved participant access to the program and allowed BOT providers to accommodate participants from beyond their own geographical catchment area, facilitating earlier completion of the program. However, because access to the Internet or a home computer is not universal and some participants are less familiar with videoconferencing technology, videoconferencing does not address all access issues. The ability to mute workshop discussion when online facilitated movement through curriculum and private virtual spaces could be used for one-on-one communication with any participant under the influence of alcohol or drugs. Anxiety and discomfort associated with attending an addictions treatment centre in the company of strangers was alleviated. Instead of one 8-hour in-person day, the two 4-hour online days were perceived by facilitators as more manageable and less rushed. While facilitators noted a steep learning curve in use of videoconferencing software, technology malfunctioning sometimes posed a challenge. It was also more difficult to build rapport and create connections with participants in an online setting. CONCLUSIONS: Facilitators mostly agreed that BOT participants likely benefit as much from the program online as they do in-person, and suggested that online workshops should continue because the benefits outweigh the disadvantages. Facilitators also recommended that in-person workshops be offered for those who cannot access online platforms.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".