Exploring the impact of COVID-19 on families of children with developmental disabilities: A community-based formative study
Bibliographic record
Abstract
Background: COVID-19 continues to disproportionately impact families of children with developmental disabilities (DD). There is an urgent need to understand these families' experiences, particularly those that face economic or social marginalization. This qualitative study sought to identify the experiences of families of children with DD during the COVID-19 pandemic. Methods: Using phenomenology, in-depth interviews (IDIs) were conducted with caregivers and health care providers of children with DD living in a large urban Canadian city. Interviews were recorded, transcribed, and coded using inductive coding methods by two independent coders. Transcripts were analyzed within and across stakeholder groups using thematic analysis. Results: A total of 25 IDIs were conducted in 2020. 3 main themes and 7 sub-themes emerged related to the experiences of parents and health care providers for children with DD: families reported difficulty adhering to public health measures leading to isolation and increased parental stress; restricted access to in-person services worsened behaviour and development; and worsened household financial security in already marginalized families. Conclusions: Our study demonstrates that families of children with DD have been negatively impacted by the evolving environment from the COVID-19 pandemic, and even more so in those who face social and economic challenges. Public health restrictions have impaired the daily lives of these families and our study suggests that limitations to accessing in-person services may have long-lasting impacts on the well-being of families of children with DD. It is imperative that the unique needs of these families be considered and centred for future interventions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".