Mental health and travel: \nReport on a survey
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».