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Record W3061397418 · doi:10.1093/pch/pxaa068.081

82 This is My Child: Piloting and Evaluating a Special Needs Screening Tool for Pediatric Emergency

2020· article· en· W3061397418 on OpenAlexaff
William Craig, Christopher Kilmer, David Nicholas, Mandi Newton, Lonnie Zwaigenbaum

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHealth careEmergency departmentMedicineAutism spectrum disorderMedical homePediatric Nurse PractitionerFamily medicineAutismPsychologyNursingPsychiatryNurse practitionersPrimary care

Abstract

fetched live from OpenAlex

Abstract Background Parents of children with developmental disabilities (DD) including autism spectrum disorder (ASD) or behavioural conditions can be reluctant to inform the medical team of their child’s diagnosis during a visit to the pediatric Emergency Department (PED). Knowing the child’s sensitivities and needs, however, could make it easier for the healthcare team to provide the best possible care. A one-page tool, “This is My Child”, was developed to bridge this gap. Objectives Evaluate the ease of use, benefit to child’s care, and overall acceptance of “This is My Child” from parental and health provider perspectives. Design/Methods “This is My Child” was modified from an inpatient tool which helped communication between staff and children with ASD and their families. Developed with input from PED healthcare providers, the tool entailed ten questions pertinent to children who have communication and sensory processing challenges, as well as a prompt for additional comments. Families were recruited in the waiting room, and the study was open to all who were willing to participate. The completed tool was attached to the front of the ED chart prior to the child being seen. At the end of the ED visit, parents and treating healthcare providers completed a questionnaire evaluating the tool. Recruitment was deemed complete once 30 children with a prior diagnosis of a developmental/behavioural condition had been enrolled. Following analysis of the questionnaires, focus groups were held with participating ED healthcare providers. Results Of 336 study participants recruited, 199 parents, 225 physicians and 135 nurses returned questionnaires. The large majority of parents, physicians and nurses indicated ‘Strongly or Somewhat Agreed’ that the tool was easy to understand. However, only 18% of physicians and 29% of nurses, yet 76% of parents, felt that the tool should be used for all children seen in the PED. This discrepancy between health care team and parental opinion was explored in the focus groups. Healthcare providers noted that the tool was beneficial in cases where unique developmental needs were reported. Concern was raised given the tool’s perceived lack of relevance for children without special needs, as well as additional staff work for tool utilization. Conclusion This easy to use form was welcomed by families, but less so by physicians. Work is ongoing to integrate these perspectives.

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)0.024
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.089
GPT teacher head0.397
Teacher spread0.309 · 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 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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