Mental health and travel: \nReport on a survey
Bibliographic record
Abstract
A quarter of all adults in England have been diagnosed with at least one mental illness according to the Health Survey of England, and many more say they have experienced mental illness without being diagnosed. The Centre for Transport Studies at University College London (UCL) has carried out an on-line survey of people with mental health conditions at in order to establish the difficulties that people with mental health conditions have when travelling and to identify ways in which these can be overcome. There were 385 respondents to the survey, all of whom had one or more mental health conditions. Analysis of the results from the survey has produced a number of findings: • 90% of the 385 survey respondents have anxiety issues and 68% have depression; 71% of them have panic attacks, 51% have difficulty communicating and 45% have memory loss; • The main cause of anxiety when travelling is the attitudes and behaviour of other people, particularly ‘What other people think about me’; • Having to talk to staff such as bus drivers makes nearly half of them anxious; • Another major cause of anxiety is finding the way without becoming lost; • 40% of them are anxious about finding suitable toilets when travelling, particularly older people; • Over a third of them are frequently unable to leave home because of their mental health, and this happens to nearly all of them some of the time; • Over half of them cannot buy rail tickets in advance because they do not know how they will feel on the day of travel, so they miss out on the cheapest rail fares for some journeys; • The Underground is the form of travel fewest of them are able to use; • About half are unable to travel by bus and train because of their mental illness; • Apart from better behaviour by their fellow travellers, factors that would encourage them to travel more by bus and train are clearer information before and during travel, better trained staff, and, in the case of train, being able to contact a member of staff in person when on board; • Very few of the respondents possess travel assistance cards, concessionary bus passes, ‘Please offer me a seat’ badges, Disabled Persons Railcards, or ‘Blue Badges’ for car parking or have received travel training; many of them say that these initiatives would encourage them travel more if they received them; • Only 7% of them have used ‘Passenger Assist’ to help them make rail journeys, but over half of these have found it unsatisfactory at least some of the time; • Using taxis suits many of them, but some are put off by having to chat to the driver; • Nearly 70% use mobile phone apps to help find the way, particularly the younger respondents; • Google Maps is the most popular app/website, used by over half of those who use apps when travelling; • About 30% of the respondents who wish to be employed are not; • This situation seems to be worse in rural areas, as does the quality of transport provision. 39 recommendations are put forward to address the issues identified in the work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".