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Record W2003610134 · doi:10.1177/1367493507082756

Childhood chronic pain and health care professional interactions: shaping the chronic pain experiences of children

2007· article· en· W2003610134 on OpenAlexaff
Melissa Dell'Api, Janet E. Rennick, Christina Rosmus

Bibliographic record

VenueJournal of Child Health Care · 2007
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsFeelingChronic painHealth professionalsHealth careQualitative researchPerceptionMedicineNursingPsychologyPhysical therapySocial psychology

Abstract

fetched live from OpenAlex

Children with chronic pain meet numerous healthcare professionals during their search to understand their pain. Through semi-structured interviews, this qualitative study sought to understand the experiences of five children with chronic pain as they encountered healthcare professionals. In the majority of these interactions, children reported feeling misunderstood, disbelieved and abandoned. The findings of this study demonstrate that children's experiences with professionals influence their approach towards current and future healthcare encounters. All children discussed their guarded relationships with healthcare providers. Children also developed negative perceptions about their pain, in particular believing that their experience with chronic pain was life-threatening, and demanded major life adjustments. Interactions with healthcare professionals have a tremendous influence on children's perceptions and chronic pain experiences. In order to better understand and care for children with chronic pain, it is essential that healthcare professionals provide children with the opporunity to communicate their unique experiences with pain.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.003
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 head0.015
GPT teacher head0.356
Teacher spread0.341 · 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

Citations44
Published2007
Admission routes1
Has abstractyes

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