Exploring the Views of Osteogenesis Imperfecta Caregivers on Internet-Based Technologies: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic condition that can lead to frequent debilitating bone fractures. Family caregivers of children with OI face unique challenges in providing care, which may include limited access to information about the condition, feelings of distress, and experiences of social isolation. Internet-based technologies (IBTs) have been useful for supporting other types of caregivers. However, the views of OI caregivers on IBTs have not been explored. OBJECTIVE: This study aimed to explore the views of OI caregivers on the uses of IBTs to support them in caring for their children with OI. METHODS: A qualitative descriptive study was conducted. Caregivers of children with OI were recruited at a pediatric hospital in Montreal, Canada. Interviews were used to explore each caregiver's views on the applicability of IBTs in supporting their caregiving needs. The interviews were transcribed verbatim and thematically analyzed. RESULTS: A total of 18 caregivers participated. The caregivers shared that IBTs were useful for facilitating the following activities: daily activities of caregiving (such as providing physical care, supporting relationships, supporting self-care and hope, and managing the logistics of caregiving), OI medical information seeking, and OI social networking. However, they also revealed concerns about the health consequences of IBT use and the quality of IBT content. Concerns regarding IBTs varied somewhat with caregivers' geographies. Caregivers offered suggestions and strategies for how IBTs can be optimized for caregiving. CONCLUSIONS: Family caregivers of children with OI face unique challenges in providing care, which may include lacking access to information about the rare condition and feeling socially isolated. OI caregivers use IBTs to overcome some of these challenges and to support their specific caregiving needs. These findings contribute to the paucity of knowledge by offering varied IBT strategies to support caregiving activities, which may be beneficial for other caregivers. Participants' suggestions for IBT services can inform the development of new resources for OI caregivers and potentially for other caregivers of children living with rare conditions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 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".