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Record W4307054447 · doi:10.1093/pch/pxac100.073

74 Patient-oriented research priorities for pediatric hospital care in Canada: a James Lind Alliance Priority Setting Partnership

2022· article· en· W4307054447 on OpenAlexaffabout
Peter J. Gill, P Bayliss, Karen Breen‐Reid, Francine Buchanan, Kim De Castris‐Garcia, Mairead Green, Shelley Frappier, Michelle Quinlan, Noel Wong, Katherine Cowan, Sanjay Mahant

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversity of TorontoTrillium Health CentreHospital for Sick Children
Fundersnot available
KeywordsGeneral partnershipInterimMedicineAllianceFamily medicineNursingPolitical science

Abstract

fetched live from OpenAlex

Abstract Background The largest population of paediatric patients cared for in hospital are those with medical conditions managed in general paediatric inpatient units (GPIUs). Despite the large burden, there is a lack of high-quality research focused on the care of children and youth admitted to GPIUs. The Canadian Paediatric Inpatient Research Network (PIRN) was launched in 2019 to generate evidence to improve the care and outcomes for hospitalized children in GPIUs. To help establish a future research agenda, PIRN brought together clinicians, youth, patients and caregivers to identify important clinical management questions about the care of children in hospital. Objectives To conduct a research priority setting study to identify the most important unanswered clinical management research questions about paediatric hospital care in GPIUs that are important to patients, caregivers, and clinicians. Design/Methods Established James Lind Alliance Priority Setting Partnership methodology was utilized for this project, which was overseen by a Steering Group composed of patient partners, (youth, parents of patients) and clinicians (nurses, and paediatricians) (n=10), facilitated by a JLA Adviser. In phase 1, an online REDCap survey was administered to collect unanswered research questions regarding paediatric hospital care via 3 open-ended questions. Responses from the phase 1 survey went through a detailed evidence checking process. Subsequently, unanswered research questions were brought forward to a phase 2 interim prioritization online REDCap survey. A final list of top 10 unanswered research questions pertaining to paediatric hospital care was established at the final priority setting workshop. Results The phase 1 survey was completed by 188 participants and generated 495 unanswered research questions and comments, of which 58 were deemed out of scope. The remaining 437 responses were grouped into themes (e.g., hospital policy, communication, shared-decision making, health service delivery and health service management), and then refined to 75 unanswered research questions. Of these 75, 4 questions had sufficient evidence, and 21 were submitted by only one respondent. Fifty unanswered research questions were included in the phase 2 survey, which was completed by 201 participants. The top 16 questions – the top 10 from both patient partners (youth, parents of patients) and clinicians respectively – were presented at the final priority setting workshop and the top 10 questions were prioritized (Table 1). The top 10 questions focus on the care of special inpatient populations (e.g. children with medical complexity), communication, shared-decision making, support strategies, mental health supports, shortening length of stay, and supporting Indigenous patients, parents and families. Conclusion The top 10 unanswered questions on paediatric hospital care will help guide future research. PIRN will use these prioritized questions to generate scientific knowledge to improve the outcomes of hospitalized children and youth in GPIUs through the conduct of patient-oriented research.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.005
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 head0.043
GPT teacher head0.380
Teacher spread0.337 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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