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Record W2806862488 · doi:10.1111/jpc.13878

How to prevent bullying

2018· editorial· en· W2806862488 on OpenAlexaboutno aff
David Isaacs

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2018
Typeeditorial
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsNothingMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0040.006
Open science0.0020.004
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0330.016

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.

Opus teacher head0.016
GPT teacher head0.338
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations3
Published2018
Admission routes1
Has abstractyes

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