What Comes Before Report Writing? Attending to Clinical Reasoning and Thinking Errors in School Psychology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Psychoeducational assessment involves collecting, organizing, and interpreting a large amount of data from various sources. Drawing upon psychological and medical literature, we review two main approaches to clinical reasoning (deductive and inductive) and how they synergistically guide diagnostic decision-making. In addition, we discuss how the use of both mental shortcuts (i.e., heuristics) and cognitive biases, which we collectively refer to as thinking errors, can lead to errors in judgment when analyzing data. In particular, we highlight where and how common thinking errors may interfere with school psychologists’ reasoning throughout the assessment process. Last, we make suggestions on how to reduce errors in judgment and improve clinical reasoning skills by focusing on training, supported clinical practice, and personal strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it