Positioning Practices of Orientation and Mobility Specialists When Teaching Street Crossings: Is There a Standard Approach?
Bibliographic record
Abstract
Introduction: An important skill for orientation and mobility (O&M) specialists to have is to monitor clients appropriately when they are learning to cross intersections. Techniques books provide some suggestions for positioning during street crossings, but no research has been conducted about consensus or priorities for making appropriate decisions on positioning. The purpose of this study was to investigate general positioning decisions using visual monitoring techniques. Method: A total of 234 participants (practicing O&M specialists, preservice O&M students, and O&M university personnel) completed a 40-question survey. The survey included demographic questions, diagrams of intersections that participants used to select positioning locations, questions about lanes of threat, and questions about important factors to consider when positioning to monitor safety. Commonality of selections were analyzed and compared with demographic information. Results: The greatest consensus was found for all intersection types when the client is positioned on the corner waiting to cross and for identification of the first lane of threat. More variable position selections were made for monitoring during the crossings, and the second and third lane of threat selections were also more variable. Factors respondents indicated as most important to consider when positioning aligned with their positioning choices overall. Discussion: Personnel preparation programs may want to consider to what extent they teach considerations for positioning before and during crossings, and whether the predominant tendency to put oneself between the client and traffic warrants additional conversation. Future research should look at more complex intersections and the additional nuances used to make positioning choices. Implications for practitioners: Practitioners should reflect on whether they actively change their positioning decisions based on the situation and type of intersection versus tending to use a standard strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".