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Record W3206606467

Exploring the Needs of Parents with a Child with Chronic Pain: A Qualitative Secondary Analysis

2020· article· en· W3206606467 on OpenAlexaboutno aff
Dominique Lefèbvre, Anne Le, Jude Spiers, Shannon D. Scott

Bibliographic record

VenueInternational journal of nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsChronic painQualitative researchMedicineCoding (social sciences)Health careDescriptive statisticsFamily medicineNursingPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Background: Pediatric chronic pain affects 15-39% of children and their families, yet it remains under-recognized and undertreated by clinicians.  Despite increasing numbers of children diagnosed with chronic pain, few qualitative studies have explored the experiences and needs of these families. Methods: A secondary analysis of 13 semi-structured interviews was conducted using a qualitative descriptive approach. These interviews featured parents of children attending a large children’s hospital in a major urban city in western Canada. Data was analyzed in three phases: coding, categorizing, and developing themes. Results: Four distinct parental needs were identified: increased awareness about pediatric chronic pain, faster access to care, validation of their child’s pain, and healthcare supports throughout their journey. These themes interplayed throughout the data and shaped parents’ ability to access appropriate care and support for their child with chronic pain. Conclusion: Unique challenges prevent parents from accessing timely care for their child with chronic pain. Increasing awareness about pediatric chronic pain can help parents gain validation for their child’s pain and result in faster access to care. Our findings highlight how parents with a child with chronic pain would benefit from additional supports to help them navigate the healthcare system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.082
GPT teacher head0.356
Teacher spread0.274 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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