A longitudinal investigation of school absenteeism and mental health challenges among Canadian children and youth in the COVID-19 context
Bibliographic record
Abstract
School absenteeism across the globe has risen dramatically since the COVID-19 pandemic. Literature indicates that children and youth of all ages are struggling to attend school regularly, leading to problematic outcomes both concurrently and across time. As well, research demonstrates that children and youth who experience mental health challenges are at greater risk of increased school absenteeism rates. The present study investigated the school attendance patterns of Canadian children and youth and the longitudinal and bidirectional links with mental health challenges within the COVID-19 pandemic context. The study sample consisted of 72 children and youth, using parent reports. Parents were asked to complete an online questionnaire which included questions about the demographic characteristics of themselves and their child, their child's school attendance patterns, and their child's mental health challenges. Preliminary descriptive statistics were run in relation to school absenteeism. Two separate path analyses were conducted to determine the longitudinal links between school absenteeism and mental health (split into externalizing and internalizing behaviours) across two timepoints (Time 1 [T1]: Fall 2022, Time 2 [T2]: Spring 2023). These analyses indicated concurrent links between mental health difficulties and school absenteeism. Importantly, path analyses also showed that absenteeism at T1 predicted poorer mental health at T2, indicating that school absenteeism may be one of the driving factors in the causal relationship. A bidirectional effect was found between externalizing behaviours at T1 and absenteeism rates at T2. The reasons for school absenteeism were examined across each time point and for both the externalizing and internalizing groups separately. The present study highlights the complex interplay between mental health and school absenteeism in the context of the COVID-19 pandemic. It provides avenues for effective intervention to better support children and youth struggling with mental health and school absenteeism.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".