Evaluating the HEADS-ED Screening Tool in a Hospital-Based Mental Health and Addictions Central Referral Intake System: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: We evaluated the use of a mental health (MH) screening tool in a hospital-based centralized MH referral telephonic intake process. The tool is used to guide psychosocial screening in several domains: home; education; activities and peers; drugs and alcohol; suicidality; emotions, thoughts, and behaviors; and discharge resources (HEADS-ED). We wanted to understand the use of the tool to guide next step in care decision-making over the telephone. METHODS: Intake workers used the HEADS-ED tool to guide the assessment processes, identified areas of MH need, and made decisions about next step in care. We completed a retrospective chart review of all callers to the intake system over 4 months to gather initial decision at intake and subsequent steps in treatment. χ2 and analysis of variance tests were used to examine differences between HEADS-ED scores and next step in care. RESULTS: A total of 674 patients aged 3 to 19 years (mean age = 11.7 years, SD = 0.6; girls = 50.0%) called for services. Significant mean differences were found on total HEADS-ED scores between treatment options (F4,641 = 75.76; P < .001). Decision validity indicated that 86% (n = 506 of 587) of initial referrals matched treatments that were actually received. Uptake of the tool was 100%, and interrater reliability indicated strong agreement between raters (intraclass correlation coefficient = 0.82; P < .001). CONCLUSIONS: With our results, we support the use of the HEADS-ED tool in a telephone-based MH intake system to help guide the initial assessment and inform decision-making about fit of next step in care, both within the health center–based MH system and in the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".