Notice bibliographique
Résumé
Natalie Hampton was 12 years old when she started at her new school, keen to make friends.1 But ‘everyone already had friends and they weren't looking for any more’.1 At lunchtime, she tried to join other tables but was told to go away. Within a year, she was the social outcast, the ‘untouchable’, with no friends. The other children called her names and threatened her. In a classic example of victim-blaming, the school administration and even the school counsellor were convinced it was Natalie's fault. She was ‘drawing the fire’. With the school doing nothing to stop them, the bullies moved from taunting to physical violence. Natalie became increasingly anxious and depressed, her sleep was disturbed, she had nightmares and developed somatic symptoms of headaches and stomach aches. Of course this was fault of both the school and the nasty little bullies. The story has a happy ending. Natalie changed school after 2 years. On her first day at her new high school, a student saw she was new and alone and befriended her. Natalie says in a TED talk she has given that ‘It saved my life.’ Natalie became increasingly confident and sure of her own social worth. She had lots of friends and her physical ills disappeared. Most importantly, she learned from her escape from social isolation and was determined to stop it happening to others. Every time she saw a child eating lunch alone, Natalie would invite the child to join her friends at their table. She created a mobile phone app, ‘Sit With Us’. She became famous in the USA, where this happened, and People magazine included her as one of the ‘25 Women Changing the World’. We should not think: ‘Oh well, we know about bullying in the USA. That rarely happens in my country’. The Organisation for Economic Co-operation and Development (OECD) recently published data from 2015 on 15-year-old students from 53 OECD countries. Regarding bullying, New Zealand was ranked the second worst of 53, behind only Latvia.2 Australia came 5th, the UK 6th, Canada 7th and the USA 19th. Korea was 53rd and best. The ranking is based on a score derived from asking students how often in the past year other students had excluded them on purpose, mocked them, threatened them, taken or damaged their possessions, hit them or spread nasty rumours about them. In the OECD data, 4% of students reported physical bullying and 11% were made fun of several times a month. Girls were less likely to suffer physical abuse, but more likely to suffer from the spreading of nasty rumours. New immigrants were more likely to be the victims of all types of bullying. Students who were bullied were more likely to play truant. They performed worse academically, although whether this was a cause of bullying or a result of it is unclear. Bullied students reported less satisfaction with life than other students. In Australia and New Zealand, about a quarter of all the 15-year-old students reported experiencing bullying in the previous year. The OECD report discusses cyber-bullying: nasty text messages, chats or comments, and either spreading rumours on-line or excluding victims from on-line conversation. Cyber-bullying follows the victim home, so there may be no escape at the end of the school day. Girls are more likely than boys to be victims and perpetrators of cyber-bullying.2 An effective way to reduce cyber-bullying is for schools to require children to put their phones in a locked box until the end of the day. The way schools try to prevent bullying and the way they react to it when it does occur is critically important. It is all very well shaking our heads and tut-tutting about bullying, but what can we do to prevent it? A range of research initiatives and policy changes have arguably done little or nothing to reduce the levels of bullying in schools. The inspirational Natalie Hampton has shown what can be done at an individual level by students. She may have found the key to effecting community changes in behaviour, which is to engage students. In a cluster randomised study in 56 middle schools in New Jersey (24 191 children aged 11–15), an average of 26 ‘seed’ students from each intervention school were assigned to an intervention where they were encouraged to take a public stance against conflict at school.5 The seed students were offered optional support in their activities by the research team. A trained research assistant met the seed group every 2 weeks to identify common conflict behaviours. The seed team created hash-tag slogans of conflict behaviours and made posters addressing conflicts, with the seed students' photos posted adjacent. The seed students gave orange wristbands with the intervention logo, a tree, to reward students who behaved in a friendly and conflict-mitigating manner. Over a 1-year period, reported school conflict was reduced by 30% in intervention compared with control schools. Seed students who were more popular with their peers (the researchers call them ‘social referents’ with increased ‘social capital’) were most effective at reducing conflict. 5 The main message of this editorial is bullying in schools, but bullying in hospitals is also a major issue. It starts at medical school6 and persists into clinical settings.7, 8 In recent surveys in public hospitals in Australia and New Zealand, a third or more of staff report experiencing bullying in the workplace.7, 8 Paediatricians fare little better than their colleagues: 30% of NZ paediatricians report being bullied at work.8 It includes sexual harassment.9 A common theme of the medical workplace bullying literature is that senior management do not address the issue adequately when bullying is reported. The New Jersey lesson needs to be brought to the medical workplace: influential hospital opinion leaders need to stop their implicit complicity and stand up to bullying in all its forms. Whenever we witness bullying, we need to be brave and speak up, there and then, and confront the bully. Peer pressure is the best pressure. The author thanks Anna Isaacs, Meryta May, Ken Nunn, Fenton O'Leary, Karen Scott and Steve Isaacs for their invaluable help in preparing this manuscript.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,016 |
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 ».