Teacher Ratings of Three School Psychology Report Recommendation Styles
Bibliographic record
Abstract
Educators are primary consumers of information provided in school psychology reports. There is disagreement in the literature as to whether teachers prefer briefer recommendations as compared to more detailed and specific recommendations. Specific recommendations can been seen as prescriptive and leading to higher requirements for accountability by educators. This study initially assessed teachers’ opinions concerning the level of detail provided in a model report with recommendation sections presented with low, medium, and high levels of specificity. Participants were 102 certified teachers taking continuing education courses. Each read a fictional report followed by the three recommendation styles presented in varied orders. Teachers rated each style for relevance, individualization, clarity, and likelihood for use. They indicated which style would be preferred for a child in their classroom, in preparing an Individual Education Plan (IEP), and for use with parents. The low specificity recommendations received lower ratings, while the medium and high levels did not differ. Overall, teachers preferred the highest specificity recommendations for a student in their class and for funding applications. For use with parents there was a slight preference for medium specificity recommendations. Teachers also indicated that the inclusion of the specific format elements surveyed is beneficial and that detail is preferred over brevity. In light of these findings, school psychologists should feel comfortable in providing detailed recommendations that mirror a well crafted IEP.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".