Evaluating a telehealth intervention for urinalysis monitoring in children with neurogenic bladder
Bibliographic record
Abstract
Telehealth as a community-monitoring project within children's urology care is an innovative development. There is limited evidence of the inclusion of staff and parents in the early-stage development and later adoption of telehealth initiatives within routine urological nursing care or families' management of their child's bladder. The aim was to explore the experiences of key stakeholders (parents, clinicians, and technical experts) of the proof of concept telehealth intervention in terms of remote community-based urinalysis monitoring by parents of their child's urine. A concurrent mixed-methods research design used soft systems methodology tools to inform data collection and analysis following interviews, observation, and e-surveys with stakeholders. Findings showed that the parents adopted aspects of the telehealth intervention (urinalysis) but were less engaged with the voiding diary and weighing. The parents gained confidence in decision-making and identified that the intervention reduced delays in their child receiving appropriate treatment, decreased the time burden, and improved engagement with general practitioners. Managing the additional workload was a challenge for the clinical team. Parental empowerment and self-efficacy were clear outcomes from the intervention. Parents exercised their confidence and control and were selective about which aspects of the intervention they perceived as having credibility and which they valued.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".