Readability of Commonly Used Quality of Life Outcome Measures for Youth Self-Report
Bibliographic record
Abstract
Self-report measures are central in capturing young people's perspectives on mental health concerns and treatment outcomes. For children and adolescents to complete such measures meaningfully and independently, the reading difficulty must match their reading ability. Prior research suggests a frequent mismatch for mental health symptom measures. Similar analyses are lacking for measures of Quality of Life (QoL). We analysed the readability of 13 commonly used QoL self-report measures for children and adolescents aged 6 to 18 years by computing five readability formulas and a mean reading age across formulas. Across measures, the mean reading age for item sets was 10.7 years (SD = 1.2). For almost two-thirds of the questionnaires, the required reading age exceeded the minimum age of the target group by at least one year, with an average discrepancy of 3.0 years (SD = 1.2). Questionnaires with matching reading ages primarily targeted adolescents. Our study suggests a frequent mismatch between the reading difficulty of QoL self-report measures for pre-adolescent children and this group's expected reading ability. Such discrepancies risk undermining the validity of measurement, especially where children also have learning or attention difficulties. Readability should be critically considered in measure development, as one aspect of the content validity of self-report measures for youth.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".