Training teachers in classroom management to improve mental health in primary school children: the STARS cluster RCT
Bibliographic record
Abstract
Background Poor mental health in childhood is common, persistent and associated with a range of adverse outcomes that include persistent psychopathology, as well as risk-taking behaviour, criminality and educational failure, all of which may also compromise health. There is a growing policy focus on children’s mental health and the role of schools in particular in addressing this. Objectives To evaluate whether or not the Incredible Years ® (IY) Teacher Classroom Management (TCM) training improved children’s mental health, behaviour, educational attainment and enjoyment of school, improved teachers’ mental health and relationship with work, and was cost-effective in relation to potential improvements. Design A two-arm, pragmatic, parallel-group, superiority, cluster randomised controlled trial. Setting A total of 80 UK schools (clusters) were recruited in three distinct cohorts between 2012 and 2014 and randomised to TCM (intervention) or teaching as usual [(TAU) control] with follow-ups at 9, 18 and 30 months. Schools and teachers were not masked to allocation. Participants Eighty schools ( n = 2075 children) were randomised: 40 ( n = 1037 children) to TCM and 40 ( n = 1038 children) to TAU. Interventions TCM was delivered to teachers in six whole-day sessions, spread over 6 months. The explicit goals of TCM are to enhance classroom management skills and improve teacher–student relationships. Main outcome measures The primary planned outcome was the teacher-reported Strengths and Difficulties Questionnaire Total Difficulties (SDQ-TD) score. Random-effects linear regression and marginal logistic regression models using generalized estimating equations were used to analyse outcomes. Results The intervention reduced the SDQ-TD score at 9 months [adjusted mean difference (AMD) –1.0, 95% confidence interval (CI) –1.9 to –0.1; p = 0.03] but there was little evidence of effects at 18 months (AMD –0.1, 95% CI –1.5 to 1.2; p = 0.85) and 30 months (AMD –0.7, 95% CI –1.9 to 0.4; p = 0.23). Planned subgroup analyses suggested that TCM is more effective than TAU for children with poor mental health. Cost-effectiveness analysis using the SDQ-TD suggested that the probability of TCM being cost-effective compared with TAU was associated with some uncertainty (range of 40% to 80% depending on the willingness to pay for a unit improvement in SDQ-TD score). In terms of quality-adjusted life-years (QALYs), there was evidence to suggest that TCM was cost-effective compared with TAU at the National Institute for Health and Care Excellence thresholds of £20,000–30,000 per QALY at 9- and 18-month follow-up, but not at 30-month follow-up. There was evidence of reduced disruptive behaviour ( p = 0.04) and reductions in inattention and overactivity ( p = 0.02) at the 30-month follow-up. Despite no main effect on educational attainment, subgroup analysis indicated that the intervention’s effect differed between those who did and those who did not have poor mental health for both literacy (interaction p = 0.04) and numeracy (interaction p = 0.03). Independent blind observations and qualitative feedback from teachers suggested that teachers’ behaviour in the classroom changed as a result of attending TCM training. Limitations Teachers were not masked to allocation and attrition was marked for parent-reported data. Conclusions Our findings provide tentative evidence that TCM may be an effective universal child mental health intervention in the short term, particularly for primary school children who are identified as struggling, and it may be a cost-effective intervention in the short term. Future work Further research should explore TCM as a whole-school approach by training all school staff and should evaluate the impact of TCM on academic progress in a more thorough and systematic manner. Trial registration Current Controlled Trials ISRCTN84130388. Funding This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research ; Vol. 7, No. 6. See the NIHR Journals Library website for further project information. Funding was also provided by the NIHR Collaboration for Leadership in Applied Health Research and Care South West Peninsula (NIHR CLAHRC South West Peninsula).
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".