Understanding the relationship between chronic health conditions, school absence and educational attainment in UK secondary schools: a qualitative study protocol
Bibliographic record
Abstract
Around a quarter of young people aged 11 to 15 reported that they had a long-term illness or disability lasting at least 6 months in the Health Behaviour in School-aged Children (HBSC) study in England in 2018. [1] Similar figures (27.5%) were reported for 11- to 16-year-olds who said they had a condition that was lasting or was expected to last 12 months or more in the Northern Irish Young Person’s Behaviour and Attitude Survey. [2] Of those who reported having a long-term illness or disability in the HBSC study, 30% said that their condition affected their school attendance and/or participation. Attendance at school is key to children’s health and wellbeing through enabling learning and socialising with peers. [3,4] Children with chronic health conditions are more likely to be absent from school and have poorer attainment. [5,6]. Absence can occur as a direct consequence of ill health, medical appointments, or hospitalisations, however, factors other than absence may contribute to worse educational outcomes for children with chronic conditions. These include: the nature of the condition itself; how well students are supported by school staff both emotionally and in keeping up with school work; students’ ability to self-manage their condition and their wellbeing; social support for students from their friends and classmates; and the quality of communication between teachers, parents and students. [7–13] Chronic health problems are defined in this study as any physical or mental health condition that has required healthcare input for one year or more. Although different chronic health problems may have differential impacts on young people’s ability to attend school, national policies highlight that young people with the same health condition might have diverse needs. [4,14,15]
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".