A systems relations model for Tier 2 early intervention child mental health services with schools: An exploratory study
Bibliographic record
Abstract
Over the last 15 years, policy initiatives have aimed at the provision of more comprehensive Child and Adolescent Mental Health care. These presented a series of new challenges in organising and delivering Tier 2 child mental health services, particularly in schools. This exploratory study aimed to examine and clarify the service model underpinning a Tier 2 child mental health service offering school-based mental health work. Using semi-structured interviews, clinician descriptions of operational experiences were gathered. These were analysed using grounded theory methods. Analysis was validated by respondents at two stages. A pathway for casework emerged that included a systemic consultative function, as part of an overall three-function service model, which required: (1) activity as a member of the multi-agency system; (2) activity to improve the system working around a particular child; and (3) activity to universally develop a Tier 1 workforce confident in supporting children at risk of or experiencing mental health problems. The study challenged the perception of such a service serving solely a Tier 2 function, the requisite workforce to deliver the service model, and could give service providers a rationale for negotiating service models that include an explicit focus on improving the children's environments.
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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 | 0.006 | 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 | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".