Credible knowledge: A pilot evaluation of a modified GRADE method using parent-implemented interventions for children with autism
Bibliographic record
Abstract
BACKGROUND: Decision-making in child and youth mental health (CYMH) care requires recommendations that are developed through an efficient and effective method and are based on credible knowledge. Credible knowledge is informed by two sources: scientific evidence, and practice-based evidence, that reflects the "real world" experience of service providers. Current approaches to developing these recommendations in relation to CYMH will typically include evidence from one source or the other but do not have an objective method to combine the two. To this end, a modified version of the Grading Recommendations Assessment, Development and Evaluation (GRADE) approach was pilot-tested, a novel method for the CYMH field. METHODS: GRADE has an explicit methodology that relies on input from scientific evidence as well as a panel of experts. The panel established the quality of evidence and derived detailed recommendations regarding the organization and delivery of mental health care for children and youth or their caregivers. In this study a modified GRADE method was used to provide precise recommendations based on a specific CYMH question (i.e. What is the current credible knowledge concerning the effects of parent-implemented, early intervention with their autistic children?). RESULTS: Overall, it appeared that early, parent-implemented interventions for autism result in positive effects that outweigh any undesirable effects. However, as opposed to overall recommendations, the heterogeneity of the evidence required that recommendations be specific to particular interventions, based on the questions of whether the benefits of a particular intervention outweighs its harms. CONCLUSIONS: This pilot project provided evidence that a modified GRADE method may be an effective and practical approach to making recommendations in CYMH, based on credible knowledge. Key strengths of the process included separating the assessments of the quality of the evidence and the strength of recommendations, transparency in decision-making, and the objectivity of the methods. Most importantly, this method combined the evidence and clinical experience in a more timely, explicit and simple process as compared to previous approaches. The strengths, limitations and modifications of the approach as they pertain to CYMH, are discussed.
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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 | 0.107 | 0.243 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".