Psychological assessment in school contexts: ethical issues and practical guidelines
Bibliographic record
Abstract
BACKGROUND: Psychological assessment in school settings involves a range of complexities and ethical dilemmas that practitioners must navigate carefully. This paper provides a comprehensive review of common issues faced by school psychologists during assessments, discussing best practices and ethical guidelines based on codes from various professional organizations. METHODS: We examine the entire assessment process, from pre-assessment considerations like informed consent and instrument selection to post-assessment practices involving results communication and confidentiality. Key ethical concerns addressed include fairness in assessment, cultural and linguistic appropriateness of testing materials, and issues surrounding informed consent. RESULTS: Specific challenges discussed include selecting appropriate assessment instruments that reflect the diverse needs and backgrounds of students, ensuring fairness and removing bias in testing, and effectively communicating results to various stakeholders while maintaining confidentiality. We emphasize the importance of multi-source, multi-method assessment approaches and the critical role of ongoing professional development in ethical practice. CONCLUSION: By adhering to established ethical standards and best practices, school psychologists can effectively support the educational and developmental needs of students. This paper outlines actionable recommendations and ethical considerations to help practitioners enhance the accuracy, fairness, and impact of their assessments in educational settings.
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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 | 0.153 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| 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".