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Record W3074383510 · doi:10.1093/pch/pxaa068.101

102 Optimizing the Management of Pain and Irritability in Children with Severe Neurological Impairments: A Qualitative Study

2020· article· en· W3074383510 on OpenAlexaff
Sara Rizakos, Arpita Parmar, Julia Orkin, Harold Siden

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of British ColumbiaHospital for Sick Children
Fundersnot available
KeywordsIrritabilityThematic analysisQualitative researchMedicineCoping (psychology)Content analysisHealth carePsychologyClinical psychologyNursingAnxietyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Introduction/Background Pain and irritability of unknown origin (PIUO) is reported to effect 73% of children with severe neurological impairments (SNI), and is a source of stress for children and families alike. Currently, there is no consensus among clinicians on how to manage PIUO and it is often difficult to determine the source of pain. Lacking an explanation for the source leaves clinicians unable to effectively treat the pain and increases a caregiver’s obstacles in providing care. Limited research exists on the effect of PIUO on children with SNI and their families. Objectives To explore and characterize the overall experience of PIUO for children with SNI and their families. Design/Methods Semi-structured interviews were conducted with parental caregivers of children with SNI who experience PIUO and are followed by the Complex Care Program at SickKids. Interview guide topics included pain expression and management, healthcare-team support and family coping. Interviews were conducted until saturation was reached. Interviews were audio-recorded, transcribed verbatim, coded and analyzed by two independent reviewers using an inductive six-step thematic analysis process on NVivo software. Results Fifteen caregivers were interviewed, with 93% being mothers and 33% being a visible minority. Interviews revealed two major themes and associated subthemes (in parentheses): 1) Day-to-day life with PIUO (pain expression and frequency, management, and quality of life) and, 2) Areas for improvement (diagnostic process, resources and support, healthcare-team interactions). Characterizing the PIUO experience is an important area of research as findings can be used to guide clinical teams in providing holistic family-centered care to children with SNI. The findings support the need for clinical innovation by adjusting practice guidelines through the creation and implementation of an integrated clinical pathway to identify treatable causes of pain and irritability in children with SNI. Conclusion Diagnostic tests for PIUO are often inconclusive and stressful for patient-families. Limited pharmacological and non-pharmacological treatments exist for PIUO. Parental caregivers describe the experience as emotionally challenging and requested support for coping. Future research should focus on interventions for PIUO in children with SNI and reducing caregiver stress and burden.

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)0.011
metaresearch head score (Gemma)0.015
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.006
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.301
Teacher spread0.281 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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