The association between child and adolescent emotional disorder and poor attendance at school: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression are common in young people and are associated with a range of adverse outcomes. Research has suggested a relationship between emotional disorder and poor school attendance, and thus poor attendance may serve as a red flag for children at risk of emotional disorder. This systematic review aims to investigate the association between child and adolescent emotional disorder and poor attendance at school. METHODS: We will search electronic databases from a variety of disciplines including medicine, psychology, education and social sciences, as well as sources of grey literature, to identify any quantitative studies that investigate the relationship between emotional disorder and school attendance. Emotional disorder may refer to diagnoses of mood or anxiety disorders using standardised diagnostic measures, or measures of depression, anxiety or "internalising symptoms" using a continuous scale. Definitions for school non-attendance vary, and we aim to include any relevant terminology, including attendance, non-attendance, school refusal, school phobia, absenteeism and truancy. Two independent reviewers will screen identified papers and extract data from included studies. We will assess the risk of bias of included studies using the Newcastle-Ottawa Scale. Random effects meta-analysis will be used to pool quantitative findings when studies use the same measure of association, otherwise a narrative synthesis approach will be used. DISCUSSION: This systematic review will provide a detailed synthesis of evidence regarding the relationship between childhood emotional disorder and poor attendance at school. Understanding this relationship has the potential to assist in the development of strategies to improve the identification of and intervention for this vulnerable group. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016052961.
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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 | 0.074 | 0.089 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.020 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
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".