A new model for the diagnostic assessment services trajectory for neurodevelopmental conditions
Bibliographic record
Abstract
Purpose: The Canadian province of Québec faces several issues regarding the accessibility and quality of diagnostic assessment and the efficiency and continuity of evaluation, support, and intervention services for children with neurodevelopmental conditions (NDCs). To address these issues, the Ministry of Health and Social Services mandated a research team to initiate the development of a reference trajectory, i.e., a proposed model pathway based on national and international best practices and research, for the diagnostic assessment of NDCs in children aged 0-7 years. Methods: The present study focused on the development of a logic model to operationalize the diagnostic services trajectory using a community-based participatory research approach and informed by implementation science. This involved representatives from multiple stakeholder groups (e.g., parents, professionals, physicians, administrators, researchers). Project steps included an analysis of best practices from a literature review on diagnostic trajectories, focus groups and interviews with stakeholders, and a validation process to ensure the appropriateness of the final model. Results: The integration of existing research and stakeholder input resulted in a logic model for a new diagnostic services trajectory for children aged 0-7 years suspected of NDCs and identified key ingredients that should be present in its future implementation. Conclusion: The proposed model for a diagnostic services trajectory is expected to address several systemic issues identified previously. Its implementation will need to be evaluated to ensure its sustained focus on the needs of families and its ability to promote their quality of life, well-being, and involvement.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".