Understanding the experience of adults with dyslexia: a quantitative and qualitative analysis
Bibliographic record
Abstract
Dyslexia is a neurobiological disorder characterized by persistent difficulties in reading, writing and spelling. Studying adults with dyslexia is challenging due to diverse experiences, varying ages of diagnosis and potential comorbidities. This study utilized a mixed-method approach to explore how the timing and occurrence of a dyslexia diagnosis influence individuals' experiences. Descriptive analysis revealed heterogeneous responses concerning age of diagnosis and overall experience, with predictor variables including perceived reading severity, family dyslexia diagnosis, comorbidities and self-perceptions of disability, intelligence, frustration, laziness, empowerment and self-restraint. To further investigate these associations, three classification and regression trees (CART) were constructed, showing that individuals diagnosed early or late without comorbidities were more likely to report positive or neutral experiences. A qualitative reflexive thematic analysis identified six themes: (1) internalizing effects of dyslexia, (2) perceived experience of dyslexia, (3) perceived perception by others, (4) shifts in experience over time, (5) acceptance and (6) coping skills. These qualitative findings complemented the descriptive and CART results, providing a comprehensive understanding of dyslexia experiences and secondary effects based on the age of diagnosis. This combined analysis underscores the importance of early diagnosis and the absence of comorbidities in shaping positive outcomes for individuals with dyslexia.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".