Parents’ Perspectives on Children’s Expressive Language Disorders: A Qualitative Case Study of Early Childhood Development
Bibliographic record
Abstract
This study explores the development of expressive language disorder in a five-year-old child from the perspective of a parent in Bandung Regency, Indonesia. Using a descriptive qualitative case study approach, the research follows the child’s developmental history from the prenatal period through the age of five. Data were collected through in-depth interviews conducted between January and April 2024, using version D of the Speech Participation and Activity Assessment of Children (SPAA-C) instrument. The analysis employed a descriptive analytical method, including data reduction, data presentation, triangulation, and conclusion drawing. Findings indicate that the child’s expressive language delay is influenced by a combination of prenatal, perinatal, and environmental factors. The child was born prematurely and spent the first 43 days in an incubator, resulting in limited sensory stimulation during a critical developmental window. Prenatal risk factors such as intrauterine growth restriction (IUGR), fetal distress, and severe preeclampsia (PEB) were also identified. Perinatal complications, including intestinal infection, further disrupted early feeding and sensory experiences. Environmental factors such as limited interaction during the COVID-19 pandemic, extended family misconceptions about developmental red flags, inconsistent nutritional intake, and maternal psychological stress contributed to delays in expressive language development. Despite these challenges, the child demonstrated strong receptive language skills, age-appropriate cognitive development, and positive social functioning. This research provides context-specific insights into how expressive language disorders manifest and are managed in a non-clinical, culturally embedded setting in Indonesia. The findings have practical implications for early childhood education (ECU), particularly in informing inclusive teaching strategies for children with expressive language delays. Future research is recommended to explore classroom-based intervention strategies and to extend analysis across broader populations, including variables such as genetics, gender, cognitive profiles, birth order, and socioeconomic status.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".