Phenomenological approach to childhood cataract treatment in New Zealand using semi-structured interviews: how might we improve provision of care
Bibliographic record
Abstract
PURPOSE: To understand how we might improve the provision of medical care for children with cataracts. DESIGN: A phenomenological design was employed. Semi-structured interviews were conducted to capture rich descriptions of the phenomena. Our goal in the interview and the analysis was to understand the sources of distress associated with treatment for cataract and deprivation amblyopia which (1) could be addressed by the medical community and (2) related to treatment adherence. SETTING: Interviews were conducted by a non-clinician researcher in New Zealand (NZ) in a location chosen by informants. In NZ, the red reflex screening test is performed shortly after birth, and surgery to remove paediatric cataracts is publicly funded. PARTICIPANTS: Families of children who had a history of cataract in Auckland, NZ were posted an invitation to participate. Twenty families were interviewed. RESULTS: Our analysis illustrated that informants described a wide range of experiences, from declined cataract surgery to full adherence to medical advice including years of patching for more than 4 hours a day. Across these experiences, we identified three relevant themes; timing of diagnosis, communication between the parent and clinician, and parental social support networks. CONCLUSION: The medical community may be better placed to support families dealing with childhood cataract by improving detection of childhood cataract, building appropriate communication pathways and promoting social support, with an emphasis on empathetic, individualised care.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".