Navigation for youth mental health and addictions: protocol for a realist review and synthesis of approaches and practices (The NavMAP standards project)
Bibliographic record
Abstract
INTRODUCTION: Mental health and/or addiction (MHA) concerns affect approximately 1.2 million children and youth in Canada, yet less than 20% receive appropriate treatment for these concerns. Youth who do not receive appropriate support may disengage from care and may experience lasting MHA issues. Families of these youth also support them in finding and accessing care. Thus, system supports are needed to help youth and their families find and equitably access appropriate care. Navigation is an innovation in MHA care, providing patient-centred support and care planning that helps individuals and families overcome barriers to care. Despite the increasing availability of navigation services for youth with MHA concerns, practices and models vary, and no single source has synthesised evidence regarding approaches and outcomes for this population into comprehensive standards. METHODS AND ANALYSIS: The proposed research will bring together evidence in youth MHA navigation, to establish this important system support as a factor that can enhance the integration and continuity of care for these youth. Our team, which includes researchers, administrators, clinical leads, an MHA navigator and youth and caregivers with lived experience, will be involved in all project stages. Realist Review and Synthesis methodology will be used, the stages of which include: defining scope, searching for evidence, appraising studies and extracting data, synthesising evidence and developing conclusions, and disseminating findings. ETHICS AND DISSEMINATION: Ethics approval is not required, as the study involves review of existing data. Dissemination plans include scientific publications and conferences and online products for stakeholders and the general public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.202 | 0.215 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.104 | 0.016 |
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".