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 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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".