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Record W7033501876

Quantitative and qualitative testing of DARWeb: An online self-guided intervention for children with functional abdominal pain and their parents

2019· article· en· W7033501876 on OpenAlexaff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIntervention (counseling)Abdominal painCoping (psychology)Quality of life (healthcare)Qualitative analysisQualitative research
DOInot available

Abstract

fetched live from OpenAlex

The main objective of this study was to preliminary explore the effects of DARWeb on different outcomes. A Quasi-experimental, one-group, pretest-posttest design was used. Parents and children were asked to complete questionnaires and questions (separately) about quality of life, abdominal pain severity, and satisfaction. Semi-structured interviews with families were also performed. This study focuses on 17 families. Results showed that parent's ratings of children's abdominal pain severity were significantly lower after finishing the intervention and at the 3-month follow-up, and quality of life scores had increased significantly after 3 months. From children's ratings, mean abdominal pain severity scores were significantly lower after the intervention compared to the preintervention assessment. Both parents and children were quite satisfied with the intervention. In qualitative interviews, families suggested that DARWeb helped them to give less importance to pain and to learn coping strategies. In conclusion, this study showed the potential usefulness of DARWeb for children with functional abdominal pain and for their parents.

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 score0.684
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.061
GPT teacher head0.274
Teacher spread0.213 · 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
Published2019
Admission routes1
Has abstractyes

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