EVALUATION OF THE DEMENTIA FRIENDLINESS OF AIR TRAVEL—A CANADIAN PILOT STUDY
Bibliographic record
Abstract
Abstract An earlier initiative involving various stakeholders in the air travel industry, disability advocates, researchers, and individuals living with dementia and their travel partners identified five key areas to reduce the challenges faced by travellers living with cognitive limitations: there was a need for information and awareness on the topic, a discussion on disclosure and identification, a readjustment of processes and spaces, a need for formal training and standardized processes across jurisdictions. Next, focussing on current and planned initiatives for reducing these obstacles, the research team determined what might be available to consumers living with dementia and their families at Canadian airports. This symposium presentation will focus on the design and benefits of a citizen science approach for addressing the identified obstacles and the accessibility of the stated airport initiatives through the lens of passengers with dementia and their travel partners. Moreover, a prototype tool, adapted from the citizen science tool used in other presentations in this symposium, that includes data from the findings of both the stakeholder group and the airport analyses symposium will be presented and discussed in terms of its appropriateness and usefulness to evaluate the dementia-friendliness of air travel.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".