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Record W2558771582 · doi:10.5539/ies.v9n12p1

Can Activity Projects Improve Children’s Wellbeing during the Transition to Secondary Education?

2016· article· en· W2558771582 on OpenAlexvenueno aff
Jane Akister, Hannah Guest, Sarah Burch

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersInstitute of Education, University of LondonAnglia Ruskin UniversityChildren's Trust
KeywordsAttendanceMental healthPsychologyIntervention (counseling)Developmental psychologyDistressStress managementWell-beingAcademic achievementSelf-esteemClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Promoting child mental wellbeing is an important part of UK early intervention policy. Children with poor physical or mental health have significantly lower educational attainment and lower social status as adults. ‘Activity’ projects are one form of early intervention used to try and help vulnerable children. Evidence relating to the effectiveness of activity programmes is limited and there is little to say which children benefit most. This paper reports on a summer activity project for children identified as vulnerable in the transition from primary to secondary school and is a repeat measures, longitudinal design. Reasons that children were referred to the transition project included concerns about their behaviour, school attendance, self-confidence and self-esteem. Pre-project Strengths and Difficulties Questionnaires show that most of these children have borderline or high Overall Stress scores, suggesting teachers are right to be concerned about them. The most significant improvement following the project was for children with high scores for emotional distress. There were no improvements for children referred for behavioural concerns.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.017
GPT teacher head0.341
Teacher spread0.324 · 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 designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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