Human Guide Training to Improve Hospital Accessibility for Patients Who Are Blind: Needs Assessment and Pilot Process Evaluation
Bibliographic record
Abstract
Background: People with disabilities are a priority population for health services research. People who are blind or have low vision (B/LV) are a segment of this priority population, who experience difficulty in accessing health care facilities due to architectural and navigational barriers. These barriers persist despite disability civil rights law in the United States. Objective: The purpose of this study is to report on a program that was developed to train way finders in human guide technique for people who are B/LV. Methods: This study took place at Michigan Medicine, an academic medical center in southeast Michigan. We conducted a needs assessment through cohort discovery and soliciting expert feedback. The human guide training program was developed using the PRECEDE-PROCEED health promotion program development model and targeted health care volunteers and staff. The intended components included in-person training, a web-based module, and tip sheets. Due to COVID-19, the in-person training was not implemented. We report findings from a process evaluation, measuring reach, knowledge, behavioral capability, and satisfaction pre- and postprogram. Results: In total, 87 participants completed the training, and most of them were Michigan Medicine volunteers. There were significant improvements in behavioral capability related to the human guide technique. Participants were satisfied with the training and provided recommendations for more detailed demonstrations and scenarios in future training sessions. Conclusions: The training improves participants' knowledge and confidence in providing wayfinding assistance to patients who are B/LV. However, further in-person training is recommended to provide hands-on experience and detailed feedback. Addressing architectural barriers and providing accessible patient education materials is crucial for improving health care accessibility for patients who are B/LV.
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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 | 0.046 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".