Using Neuropsychological Profiling to Tailor Mental Health Care for Children and Youth: a Quality Improvement Project to Measure Feasibility
Bibliographic record
Abstract
OBJECTIVE: Precision child and youth mental healthcare has great potential to improve treatment success by tailoring interventions to individual needs. An innovative care pathway in a pediatric mental health outpatient clinic was designed to allow for neuropsychology data to be integrated in psychotherapeutic care. This paper describes the feasibility of this new pathway, including implementation outcomes, acceptability, and potential for future integration. METHOD: The target population was outpatients 6-17 years old referred for individual treatment to a tertiary outpatient mental health (OPMH) clinic. The new care pathway was co-developed by neuropsychologists and mental health practitioners. A logic model was created to guide the evaluation, which was informed by the Reach Effectiveness Adoption Implementation Maintenance framework. As part of the logic model, a stepped assessment protocol was implemented, and reports on neuropsychological function were shared with patients, caregivers, and care providers. Evaluation data were collected from phone surveys, questionnaires, a focus group, and administrative records. RESULTS: Forty-two patients scheduled to receive therapy over a 6-month period were offered the opportunity to participate in the new care pathway and 39 (93%) agreed. Self-reported outcome data showed that 83% of patients and 94% of caregivers valued neuropsychology-informed care, with some describing it as transformative. Almost all practitioners (91%) reported that the project added value to their clinical care. There were no adverse effects on participants nor the flow of patients through the system. CONCLUSIONS: Neuropsychology-informed pediatric OPMH care was feasible and well-received. Clinical effectiveness should be studied in an experimental trial.
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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 | 0.071 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".