A C.L.E.A.R. Approach to Report Writing: A Framework for Improving the Efficacy of Psychoeducational Reports
Bibliographic record
Abstract
Psychoeducational reports are the primary means for a school psychologist to communicate the results of an assessment. Although reports should be written in the most efficient and reader-friendly manner, this is not always the case. Additionally, problems in report writing have remained relatively consistent for several decades, despite recommendations on how reports should be improved. The focus of the current article is to provide an integrated and easily implemented framework for improving psychoeducational reports based on the evidence and broad recommendations currently available in the literature. Specifically, the C.L.E.A.R. Approach to report writing for practitioners is presented, with practical strategies and examples provided to illustrate the use of the model in a school-based setting.
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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 | 0.213 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".