Waiting for child developmental and rehabilitation services: an overview of issues and needs
Bibliographic record
Abstract
Concern about the length of time that children, young people, and families may have to wait to access assessment, diagnostic, interventional, therapeutic, and supportive child developmental and rehabilitation (CDR) services is widespread, but adequate data collection and research on this issue remain limited. We review key concepts and issues relevant to waiting for CDR services from the published literature, a national workshop devoted to this topic, and international experience. We conclude that gaps in data, evidence, and consensus challenge our ability to address the issue of waiting for CDR services in a systematic way. A program of research coupled with actions based on consensus-building is required. Research priorities include acquiring evidence of the appropriateness and effectiveness of different models of intervention and rehabilitation services, and documenting the experience and expectations of waiting families. Consensus-building processes are critical to identify, categorize, and prioritize 'sentinel' components of CDR service pathways: (1) to reduce the inherent complexity of the field; (2) to create benchmarks for waiting for these respective services; and (3) to develop definitions for wait-time subcomponents in CDR services. Collection of accurate and replicable data on wait times for CDR services can be used to document baseline realities, to monitor and improve system performance, and to conduct comparative and analytic research in the field of CDR services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".