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Record W4401481103 · doi:10.1002/imhj.22131

The HEADS‐ED under 6: Piloting a new communimetric mental health and developmental screening and triage tool for young children

2024· article· en· W4401481103 on OpenAlexaffabout
Christine Polihronis, Paula Cloutier, Lori Kempe, Joel Schryer, Mario Cappelli

Bibliographic record

VenueInfant Mental Health Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsCarleton UniversityOntario Centre of Excellence for Child and Youth Mental HealthUniversity of OttawaMental Health Research CanadaAgricultural Research Institute of Ontario
Fundersnot available
KeywordsTriageMental healthPsychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Communimetric screening tools help clinicians identify and communicate their patient's areas of need and the corresponding level of action. However, few tools exist to identify mental health (MH) and developmental needs in young children. We aimed to implement and evaluate a new communimetric MH and developmental screening tool for children under 6 (HEADS-ED Under 6) in a community MH agency in Ontario, Canada. Using a prospective cohort design, we explored how intake workers used the HEADS-ED Under 6 screening tool from November 2019 to March 2021. 94.5% of children (n = 535/566) were screened with the HEADS-ED at intake. Total HEADS-ED scores and domains were used to inform the intensity of recommended services. Three clinical domains (Eating & sleeping, Development, speech/language/motor, and Emotions & behaviors) also independently predicted a priority recommendation. The tool showed good concordance with the InterRAI Early Years for children under 4 years old. The HEADS-ED Under 6 was a brief, easy, and valid screening tool, and can be used to identify important MH and developmental domains early, rate level of action/impairment, communicate severity of needs, and help determine intensity of service required.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.063
GPT teacher head0.413
Teacher spread0.350 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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