Exploring Mothers’ Experience of a Linguistic Feedback Technology for Children at Risk of Poor Language Development: Qualitative Pilot Study
Bibliographic record
Abstract
BACKGROUND: The early language environment is important for language development and a child's life-course trajectory. Risk factors associated with poor language development outcomes in children include maternal anxiety and depression, low educational attainment, substance misuse, and low socioeconomic status. Language Environment Analysis (LENA) is a wearable technology designed to promote caregivers' engagement in supporting their children's language development. LENA provides quantitative linguistic feedback, which has been shown to improve caregiver language output, thus enhancing a child's language environment. There is limited research on the uptake of this technology by families with developmentally at-risk children. OBJECTIVE: This qualitative study aims to explore the conditions under which mothers with children at risk of poor developmental outcomes are willing to adopt the use of LENA to monitor and improve caregiver language output. METHODS: Using a qualitative interpretive design, semistructured, in-depth interviews were conducted with 8 mothers. Participants were recruited purposively to select the maximal variation of socioeconomic and ethnodemographic backgrounds. The transcribed interview data were analyzed thematically and interpretatively. Themes were mapped abductively to an extended Unified Theory of Acceptance and Use of Technology, which included contextual factors for LENA acceptance. RESULTS: Factors that influenced the intention to use LENA included both technology-specific acceptance factors and contextual factors. Technology acceptance themes included reassurance, feeling overwhelmed, and trust. These themes were mapped to performance expectancy, effort expectancy, and social influence. Contextual themes included emergent success and the intrusion of past difficulties. These were mapped to parenting self-efficacy and perceived risk. The theme of building on success described behavioral intention. Mothers were more likely to adopt LENA when the technology was viewed as acceptable, and this was influenced by parenting self-efficacy and perceived risk. CONCLUSIONS: LENA is a technology that is acceptable to mothers with children who are at risk of poor language development outcomes. Further studies are needed to establish LENA's effectiveness as an adjunct to strategies to enrich a child's early language environment.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".