Caring for a child with retinoblastoma: Experience of Ethiopian parents
Bibliographic record
Abstract
OBJECTIVE: This study explored the lived experience of parents of children with retinoblastoma. DESIGN AND METHOD: A phenomenological qualitative study design was used, and a purposive sampling technique to recruit parents of children with retinoblastoma. Semi-structured interviews were conducted to document the lived experience of participants, who were asked to narrate their experiences caring for a child with retinoblastoma, thinking back to the day they learned about their child's condition, as well as their thoughts about the future. The interviews were conducted in Amharic and Oromo language, and audio recordings were transcribed and translated to English. Data were analyzed using thematic analysis. RESULTS: Thirteen parents (seven mothers, six fathers) participated in the study. Collectively, the children of the participants represented all the stages of the retinoblastoma journey (i.e., diagnosis, treatment, remission, and recurrence). Five major themes emerged from the thematic data analysis: (a) reactions when learning the child's condition; (b) receiving health care; (c) costs of caregiving; (d) support; and (e) uncertainties. CONCLUSION: The lived experiences of parents of children with retinoblastoma revealed a significant mental health and psychosocial burden. The sources of mental distress were found to be complex and varied. Holistic care for retinoblastoma should include programs that address the biopsychosocial needs of caregivers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".