Giving Feedback: Experiences, Training, and Perspectives of School Psychologists and Psychological Associates
Bibliographic record
Abstract
Psychoeducational assessments are a cornerstone in the field of school psychology. Results from psychoeducational assessments are frequently shared in oral feedback meetings with parents and other invested parties. Feedback meetings are important in that they fulfill an ethical obligation to share assessment results. Feedback meetings also offer therapeutic benefits to recipients, including strengthening collaboration, facilitating a better understanding of a child’s needs, and maximizing the likelihood of successful interventions being implemented for the child. Despite the importance of feedback meetings, they have been largely ignored in the training of psychologists. As a result, there is a need to better understand how psychologists build capacity in this area. This case study explores the experiences, training, and perspectives of school psychologists and psychological associates in Ontario regarding giving feedback for psychoeducational assessments. Findings suggest that psychologists and psychological associates value feedback and see it as a complex process that involves customization based on various factors, as well as navigating parent emotions. Findings also suggest that psychologists and psychological associates see learning to give feedback as a career long, multi-faceted process. Implications for training and practice are offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".