HEADS-ED Under 6: A clinician-administered mental health and developmental screening and triage tool
Bibliographic record
Abstract
Objectives: Communimetric screening tools convey functional impairment and action required, helping clinicians identify and communicate patient's needs and actions within a measurement framework. We examined the feasibility of using the HEADS-ED Under 6, a communimetric mental health (MH) and developmental screening and triage tool for children under 6, within 1Call1Click.ca, a regional coordinated access and navigation program in Eastern Ontario. We explored utility validity to understand how the HEADS-ED Under 6 is used to help intake workers screen, inform their level of need decision, and establish a cut-off for recommending interventions targeting moderate levels of need or higher. Methods: Using a prospective cohort design, intake workers at 1Call1Click.ca screened all children under 6 using the HEADS-ED Under 6 from June 2021 to August 2023. Correlations and an Analysis of variance examined how total scores were associated with the level of need, and Receiver Operator Curve analysis examined sensitivity and specificity for identifying moderate levels of need or higher. Results: In total, 589 children under 6 were screened with the HEADS-ED Under 6. Utility validity was demonstrated by significant associations between level of need and HEADS-ED Under 6 domains and total scores. A total score of ≥6 supported decision-making for moderate levels of need or higher (Area Under Curve = 0.807). Conclusions: The HEADS-ED Under 6 is a brief, easy, and valid screening tool, that can be used by clinicians to identify the level of action/impairment, communicate the severity of MH and developmental needs, and determine the level of need for services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".