A Video Based Intervention to Support Children’s Social, Emotional and Mental Health
Bibliographic record
Abstract
The intention of Reflect as an innovative intervention was to enhance and highlight the building blocks of Five to Thrive (Kate Cairns Associates 2012: Respond, Cuddle, Relax, Play and Talk) and help pre-school setting practitioners see how applying more of these principles could support positive change in a child’s behaviour. The report provides background, research and theory behind the approach and intervention, and explains why it was chosen in this situation. It gives a description of how it is delivered in practice, so that it can be reproduced.Key findings suggest progress in 2 main areas; 1) children’s behaviour, emotional literacy and self-regulation and 2) change in the practitioners’ approach to managing behaviour, their perception and attitude towards the children. Results from ratings by parents and pre-school staff using a standardised measure of behaviour (Strengths and Difficulties Questionnaire), showed improvements in children’s behaviour by decreased scores in behaviour difficulties sub-scales post intervention. The Overall Stress data from staff ratings combined scores for 4 difficulty measures (emotional, behavioural concerns, hyperactivity/inattention and peer relationships) showed that 4% of children were functioning close to the ‘Average’ band pre-intervention, compared to 50% post intervention. Furthermore, staff ratings showed significant reductions to the number of children scoring at the most concerning ‘Very High’ difficulties band; 67% pre-intervention compared to 29% post intervention. All children made progress against individually set learning behaviour goals.Progress and usefulness of Reflect was also evidenced qualitatively from setting staff evaluations and perceptions of the workers who delivered the intervention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".