The HEADS-ED: Exploring the Relationship between Mental Health Acuity and Level of Service Need Decision in Children and Adolescents
Bibliographic record
Abstract
The HEADS-ED is a validated tool used by clinicians to quickly screen for mental health and addictions needs and match them to appropriate levels of care (Cappelli et al., 2012).The current study examined the relationship between HEADS-ED total score and HEADS-ED domains, and level of service need, while exploring suicidality, sex, and age as moderators.Participants included 8,753 Canadian children and adolescents aged 6-17.99,who were screened using the HEADS-ED by intake workers at 1Call1Click.ca.Findings revealed that HEADS-ED total score and each domain predicted the odds of higher service needs.Suicidality was a significant moderator, whereas sex and age were not.Potential explanations included a ceiling effect, dependence on the HEADS-ED, and a suicidality threshold.Ultimately, the HEADS-ED effectively triages young people, and should be implemented across clinical settings to prevent risk.Future research should replicate and extend these findings in different settings and populations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".