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Record W4403609281 · doi:10.3390/children11101266

Conducting Patient-Oriented Research in Pediatric Populations: A Narrative Review

2024· review· en· W4403609281 on OpenAlexafffund
Alan Cooper, Linda Nguyen, Oluwapolola Irelewuyi, Steven P. Miller

Bibliographic record

VenueChildren · 2024
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsBC Children's HospitalMcMaster UniversityMcGill University Health CentreUniversity of CalgaryMental Health Research CanadaUniversity of British ColumbiaHospital for Sick Children
FundersCanadian Institutes of Health ResearchUniversity of British ColumbiaBC Children's HospitalUniversity of Calgary
KeywordsPsychological interventionNarrative reviewNarrativePediatric researchEngineering ethicsMedicinePsychologyEngineeringNursingPsychotherapist

Abstract

fetched live from OpenAlex

It has become increasingly common for researchers to partner with patients as members of the research team and collaborate to use their lived experiences to shape research priorities, interventions, dissemination, and more. The patient-oriented research (POR) model has been adopted by both adult and pediatric health researchers. This cultural change to conducting pediatric health research brings with it new methodologies, tools, challenges, and benefits. In this review, we aim to provide guidance on how to conduct POR for pediatric populations using examples from the literature. We describe considerations for engagement before the project begins, for engagement across the research cycle, and for measurement and evaluation. We aim to show that conducting POR is feasible, beneficial, and that many common challenges and barriers can be overcome with preparation and usage of specific tools.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.757
GPT teacher head0.621
Teacher spread0.136 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes2
Has abstractyes

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