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Record W2533821176 · doi:10.1177/0829573516674308

Managing Chronic Pain in the Classroom: Development and Usability Testing of an eHealth Educational Intervention for Educators

2016· article· en· W2533821176 on OpenAlexafffund
Sará King, Jessica Boutilier, Jill Chorney

Bibliographic record

VenueCanadian Journal of School Psychology · 2016
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health CentreDalhousie UniversityMount Saint Vincent University
FundersNova Scotia Health Research FoundationMount Saint Vincent University
KeywordsUsabilityeHealthChronic painIntervention (counseling)Psychological interventionPsychologyMedical educationApplied psychologyMedicineHealth careComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Although chronic pain is relatively common in childhood, many teachers feel ill-prepared to work with students with chronic and recurrent pain in the classroom and would like to learn more about supporting these students. A web-based eHealth intervention designed to provide information about pain and pain management in the classroom was developed based on input from clinicians, and usability was tested using three groups of stakeholders (i.e., youth with chronic pain, parents of youth with chronic pain, and teachers). Preliminary testing indicated that the usability goals were met, with the majority of participants in all groups indicating that the website was easy to use and that they would either recommend it to teachers or use it themselves. Minor design and content changes were suggested and made, whereas major changes will be made in the future. Results provide encouraging preliminary support for the utility of eHealth interventions as knowledge translation and dissemination tools for educators.

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)0.017
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.369
Teacher spread0.324 · 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 designNon-randomized trial
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

Citations12
Published2016
Admission routes2
Has abstractyes

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Same venueCanadian Journal of School PsychologySame topicPediatric Pain Management TechniquesFrench-language works237,207