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Record W2132367452 · doi:10.1177/1049732308327242

Children's Pain Assessment in Northeastern Thailand: Perspectives of Health Professionals

2008· article· en· W2132367452 on OpenAlexaff
Paula Forgeron, Darunee Jongudomkarn, Joan Evans, G. Allen Finley, Somboon Thienthong, Pulsuk Siripul, Srivieng Pairojkul, Wimonrat Sriraj, Kesanee Boonyawatanangkool

Bibliographic record

VenueQualitative Health Research · 2008
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsContext (archaeology)Pain managementHealth professionalsFocus groupHealth carePain assessmentFace (sociological concept)PsychologyQualitative researchNursingMedicinePhysical therapySociologyPolitical science

Abstract

fetched live from OpenAlex

Deficiencies in pain care within the developing world are starting to be realized. Children, in particular, are vulnerable, as preliminary studies suggest that these children receive less pain treatment because of health professionals' attitudes and beliefs. This article reports on some of the findings of the first study in a larger program of research aimed at improving pediatric pain care in Thailand. Improvements in practice are not simply the result of providing evidenced-based knowledge, but a complex process that includes the context of care. Given that little is known about the pain management experiences of Thai health professionals, including the challenges they face, we used focus groups to capture their stories. Data revealed a need for both updating pain knowledge and for supporting an increased use of appropriate practices. In this article, we focus on the issues concerning the assessment of pain resulting from underrecognizing children's pain and complex issues in communicating findings of children's 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 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)0.050
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.270
GPT teacher head0.584
Teacher spread0.314 · 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.

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

Citations36
Published2008
Admission routes1
Has abstractyes

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