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Record W4224245171 · doi:10.12927/hcq.2022.26779

Partnering with Youth and Parents for Greatest Impact of Top Patient-Oriented Priorities in Pediatric Chronic Pain Research, Care and Policy

2022· article· en· W4224245171 on OpenAlexaffvenueabout
Kathryn A. Birnie, Carley Ouellette, Justina Marianayagam, Fiona Campbell, Christine Lamontagne, Paula Forgeron, Jennifer Stinson

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsChildren's Hospital of Eastern OntarioHospital for Sick ChildrenCARE CanadaNorthern Ontario Academic Medicine AssociationUniversity of TorontoMcMaster UniversityAlberta Children's Hospital
Fundersnot available
KeywordsKnowledge translationChronic painKey (lock)Best practicePain managementMedicineNursingBusinessPublic relationsPsychologyKnowledge managementPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

Our original patient-oriented research project identified the top 10 priorities for pediatric chronic pain research and care in Canada from the perspective of people with lived experience (patients), their family members and healthcare professionals through a modified James Lind Alliance Priority Setting Partnership. We undertook subsequent knowledge translation activities with youth, families, healthcare professionals, decision makers and researchers to (1) generate awareness and interest in the top 10 priorities and our partnership process, (2) facilitate collaborative dialogue and open innovation and (3) integrate and adopt the top 10 priorities into stakeholder activities. This paper describes our knowledge translation activities, outcomes and impact.

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)0.065
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.084
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0150.005
Scholarly communication0.0130.007
Open science0.0010.015
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.035
GPT teacher head0.371
Teacher spread0.336 · 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 designQualitative
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

Citations2
Published2022
Admission routes3
Has abstractyes

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