An exploration of school attendance problems experienced by children receiving mental health services
Bibliographic record
Abstract
Aim: School attendance problems (SAPs) are a concern across education systems worldwide. SAPs are disproportionally experienced by certain groups of children, in particular those with mental health difficulties. Existing literature has identified myriad factors, including those proximal and distal, that influence attendance for these children. Most studies to date have focused on linear relationships between a small number of variables and fail to differentiate between types of SAPs (Heyne et al., 2019). A broader understanding of the complex context of school attendance problems remains understudied and is the focus of the current study. Method: Using a qualitative design, we explored a) the SAP typologies and b) the individual characteristics and education-related needs associated with the school attendance problems of 15 children receiving mental health services at a community clinic. Findings: Analyses of client files indicated that a) emotionally based school avoidance was the most common typology, b) approximately half the sample experienced one type of attendance problem while half experienced multiple types, c) anxiety was experienced by all children, and d) emotional, behavioural, social, and academic needs were noted in relation to SAPs over time. Our findings reflect the complex and varied profiles of students who share the experience of having significant school attendance problems. Limitations: Limitations of our study include potential bias introduced through the multi-step data extraction process, a reliance on the judgement of clinicians, and a lack of full access to data caused by Covid-19 restrictions. Conclusions: Future research and practice would benefit from a differentiated approach to understanding, preventing, and intervening to improve attendance and broad success for students with mental health difficulties.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".