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Record W2801698185 · doi:10.1177/1367493518777294

Evaluating a telehealth intervention for urinalysis monitoring in children with neurogenic bladder

2018· article· en· W2801698185 on OpenAlexaff
Bernie Carter, Karen Whittaker, Caroline Sanders

Bibliographic record

VenueJournal of Child Health Care · 2018
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsTelehealthUrinalysisIntervention (counseling)MedicineNursingInclusion (mineral)Data collectionWorkloadFamily medicinePsychologyTelemedicineHealth care

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.402
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2018
Admission routes1
Has abstractyes

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